A blockchain-based economic dispatch method

Abstract

The application discloses a blockchain-based economic dispatching method, comprising the following steps: obtaining a distributed power supply; constructing an Ethereum private blockchain network based on the distributed power supply; obtaining distributed power supply output power based on the Ethereum private blockchain network; performing economic dispatching calculation through a gradient correction algorithm based on the distributed power supply output power; and obtaining an economic dispatching result. Through the above technical solution, the application can make the economic dispatching reach the actual global optimum in real time and safely.

Description

Technical Field

[0001] This invention relates to the field of power grid economic dispatch technology, and in particular to an economic dispatch method based on blockchain. Background Technology

[0002] Economic dispatch is crucial for maintaining stable power system operation, saving generation costs, reducing fuel consumption and carbon emissions, and is one of the fundamental problems in power systems. The economic dispatch problem is an optimization problem that, under certain constraints of the power system, allocates the total load to the output of each generating unit to minimize the total generation cost. Traditional economic dispatch generally adopts a centralized dispatch method, where the dispatch center first obtains information from all generating units, then allocates power through an economic dispatch algorithm, and finally issues dispatch commands to each generating unit. During this process, any communication problems, such as packet loss or delay, can prevent the dispatch center from obtaining accurate and complete information, thus preventing economic dispatch from converging to the optimal solution. Furthermore, as the scale of the power grid system continues to expand, the dispatch center faces severe communication and computational pressures. If the dispatch center collapses, global economic dispatch will fail, significantly impacting the system's stability and economy.

[0003] In recent years, with the shift of power grid structure from centralized to distributed, distributed economic dispatch strategies have gained increasing attention. Distributed economic dispatch treats each distributed power source as an independent intelligent agent, each with certain decision-making capabilities. After exchanging information with neighboring nodes, each agent makes autonomous decisions and adjusts its output, effectively reducing the communication and computational burden on the central node and exhibiting good robustness and scalability. However, the highly decentralized dispatching authority makes the system vulnerable to external malicious attacks, which are more difficult to identify and defend against. Furthermore, efficient distributed economic dispatch requires both minimal information exchange between agents and the accuracy of that exchanged information. Some individuals, seeking to improve their own economic interests, may exchange false information with neighboring nodes, causing the economic dispatch to fail to achieve the actual global optimum. Therefore, improving the security of distributed economic dispatch and preventing individual deception is a prerequisite for ensuring the stable operation of economic dispatch.

[0004] (1) Traditional centralized dispatching has high communication and computing pressure and low reliability. If the dispatching center fails to obtain information on all power generation units due to communication problems, economic dispatching cannot converge to the optimal solution and power generation cost cannot be minimized.

[0005] (2) In general distributed distribution network economic dispatch, the highly decentralized dispatching rights make the system vulnerable to external malicious attacks, and the attacks are more difficult to identify and defend against, posing certain security risks.

[0006] (3) In actual scheduling scenarios, some individuals may engage in individual deception in order to improve their own economic interests, that is, exchange data with neighboring nodes with deviations, so that neighboring nodes obtain incorrect scheduling results based on the deviation parameters, resulting in economic scheduling failing to achieve the actual global optimum.

[0007] (4) In the process of distributed economic dispatch of distribution network, global variables such as global power difference and incremental cost are generally required. It is inconvenient to solve and share them. It is still necessary for a node to collect the power information of all nodes and calculate them. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a blockchain-based economic scheduling method that can achieve real-time and secure global optimization of economic scheduling.

[0009] To achieve the above-mentioned technical objectives, the present invention provides the following technical solution:

[0010] A blockchain-based economic scheduling method includes:

[0011] Obtain distributed power sources, build a private Ethereum blockchain network based on the distributed power sources, obtain the output power of the distributed power sources based on the private Ethereum blockchain network, and perform economic scheduling calculations based on the output power of the distributed power sources using a gradient correction algorithm to obtain the economic scheduling results.

[0012] Optionally, the process of building an Ethereum private blockchain network includes:

[0013] The distributed power sources are mapped to blockchain nodes, and an Ethereum private blockchain network is formed based on the communication lines between the distributed power sources and the blockchain nodes. The upper architecture of the Ethereum private blockchain network is the Ethereum platform layer, which performs economic scheduling calculations based on smart contracts and consensus algorithms.

[0014] Optionally, the process of calculating economic scheduling includes:

[0015] Construct an objective function, wherein the objective function aims to minimize the total generation cost of distributed power sources;

[0016] Construct constraints, wherein the constraints include equality constraints and inequality constraints;

[0017] The economic dispatch result is obtained by iterative calculation based on the objective function, constraints, and the output power of the distributed power source.

[0018] Optionally, the objective function is the sum of the generation costs of different distributed power sources, as follows:

[0019]

[0020] in, F represents the total cost of electricity generation, P i Let F be the output power of the i-th distributed power source. i (P i ) represents the generation cost of the i-th distributed power source; a i b i c i These are the coefficients of the quadratic, linear, and constant terms of the i-th distributed power source, respectively.

[0021] Optionally, the equality constraint is:

[0022] Where P D P represents the current total load demand of the regional power distribution network. Loss This refers to losses in the power distribution network.

[0023] Optionally, the inequality constraints include economic dispatch inequality constraints, unit compliance adjustment rate constraints, and system spinning reserve constraints.

[0024] The economic scheduling inequality constraint is: P i (min)≤P i ≤P i (max);

[0025] The unit meets the adjustment rate constraint as follows:

[0026] The system rotational reserve constraint is:

[0027] Wherein, P i (min) is P i Let P be the minimum output power of the i-th distributed power source. i (max) is P i Let P be the maximum output power of the i-th distributed power source. i (k) V represents the output power of the kth turbine of unit i; i R(t) represents the maximum power adjustment in each iteration cycle; R(t) represents the system's spinning reserve capacity at a certain moment.

[0028] Optionally, the iterative calculation process includes:

[0029] Based on the output power of the distributed power source, the incremental variable is obtained;

[0030] The gradient correction amount is obtained by calculating the incremental variable through Taylor series expansion;

[0031] Based on the constraints and objective function, the incremental variable and the output power of the distributed power source are iteratively updated according to the gradient correction amount. During the iterative update process, the gradient correction amount is adjusted according to the output power of the distributed power source. When the gradient correction amount is zero, the updated incremental variable and the updated output power of the distributed power source are the economic scheduling result.

[0032] Optionally, during the adjustment of the gradient correction amount, the adjustment rate of the gradient correction amount is suppressed by a suppression strategy.

[0033] The present invention has the following technical effects:

[0034] This proposal suggests a distributed economic scheduling scheme based on blockchain and gradient correction, and designs the scheduling framework and process. By combining the distributed storage, Proof-of-Work (PoW) consensus mechanism, cryptographic algorithms, and smart contract features of the Ethereum blockchain, information sharing is facilitated, distributed economic scheduling is rapidly implemented, and individual fraud is prevented.

[0035] ① By applying blockchain technology, the necessary information can be obtained directly from the local node during the iteration process, greatly reducing information interaction with neighboring nodes, alleviating communication pressure, and preventing individual fraudulent behavior. At the same time, smart contracts and asymmetric encryption algorithms ensure the security of economic scheduling.

[0036] ② The incremental cost of this scheme can reach the global optimal power allocation in about 30 iterations, which can quickly and efficiently achieve the goal of economic scheduling.

[0037] ③ This solution can effectively cope with large fluctuations in distribution network load, sudden changes in network loss, and sudden addition or removal of nodes, and has good robustness, stability, and scalability. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the economic scheduling method provided in an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] To address the problems existing in the prior art, the present invention provides the following solution:

[0042] like Figure 1 The proposed solution is based on the Ethereum platform to build a distributed power supply blockchain network. It adopts a gradient correction algorithm with unified variables to adapt to the characteristics of blockchain technology. Iterative calculation enables the incremental cost to converge quickly, achieving the optimal power allocation of each power source and realizing distributed economic scheduling.

[0043] The economic dispatch calculation uses a gradient correction method with a unified incremental variable. This involves setting a unified global incremental variable (the incremental cost of each power generation unit), achieving rapid convergence while reducing the amount of data stored on the blockchain and alleviating the storage burden on each node. The core idea of ​​the gradient correction method with a unified incremental variable is to set a unified incremental cost for the power sources participating in the dispatch, calculate the adjustment amount of the incremental cost in each iteration based on the system power difference, and continuously update it until convergence. At this point, the power allocation of each node reaches its optimal level, and the power generation cost is minimized.

[0044] The blockchain-based distributed economic scheduling framework consists of the following layers: the bottom layer is a blockchain network composed of blockchain smart agents and communication lines, which realizes information collection, command issuance, and node communication; the second layer is the Ethereum platform layer, which is mainly used to perform economic scheduling calculations, store economic scheduling information through the LevelDB database, use smart contracts and consensus algorithms to ensure the non-interference of economic scheduling, and use asymmetric encryption mechanisms to ensure the security of node communication; the third layer relies on the Ethereum platform to realize functions such as data management, access control, node management, and account management, and control system access and node status; the top layer combines the geth client and Vue frontend to realize scheduling information display and scheduling start and stop control.

[0045] Each distributed power source is mapped to a private Ethereum blockchain node, and a private Ethereum blockchain is built using the geth client to achieve distributed economic scheduling. Unlike the public Ethereum blockchain, the private Ethereum blockchain does not involve expensive tokens and has advantages such as fast transaction speed, good real-time performance, low deployment cost, and flexibility, supporting distributed economic scheduling operations. Write permissions on the private Ethereum blockchain are held only by the participants in the economic scheduling process, and data access and recording operations also have strict permission requirements. When initiating scheduling calculations, each node directly obtains the consensus information from the previous round from its local machine, without needing to exchange information with neighboring nodes, effectively preventing individual fraudulent behavior. At the same time, the scheduling smart contract is deployed on the private Ethereum blockchain, isolated from the external environment, making the execution process more secure. The scheduling results of each round are synchronously updated to all nodes through the PoW consensus mechanism, maintaining the consistency of scheduling data on all nodes.

[0046] The above content will be explained in detail:

[0047] Each distributed power source is mapped to an Ethereum private blockchain node. An Ethereum private chain is built through the geth client, and distributed economic scheduling is achieved by combining gradient repair algorithm.

[0048] (1) Gradient correction scheduling algorithm for unified incremental variables

[0049] Under multiple distribution network constraints, the output of each distributed power source is adjusted to ensure that the incremental costs of each power source are consistent, thereby achieving optimal power allocation and minimizing the total generation cost. This paper uses a gradient correction method with a unified incremental variable for economic dispatch calculation, treating the unified global incremental cost as a consistency variable.

[0050] Suppose a power distribution network contains n distributed generation sources and m loads, where the distributed generation sources are H1, H2, ..., Hn. n Through physical lines to loads L1, L2, ..., L m powered by.

[0051] The core idea of ​​the gradient correction method with unified incremental variables is to set a unified incremental cost λ for each power source, calculate the correction amount Δλ of λ in each iteration based on the system power difference, and continuously update λ based on the correction amount until convergence. At this point, the power allocation of each node reaches its optimum, and the total power generation cost is minimized. The specific calculation process is as follows:

[0052] The objective function of economic scheduling is:

[0053]

[0054] Where F is the total power generation cost, P i Let F be the output power (i.e., output force) of the i-th distributed power source. i(P i Let be the generation cost of the i-th distributed power source.

[0055] The formula for calculating the power generation cost of each power source is as follows:

[0056]

[0057] Where a i b i c i These are the coefficients of the quadratic, linear, and constant terms for each distributed power source, i = 1, 2, 3, ..., n.

[0058] The inequality constraints for distributed economic scheduling are:

[0059] P i (min)≤P i ≤P i (max)

[0060] The equality constraints for distributed economic scheduling are:

[0061]

[0062] Where P D P represents the current total load demand of the regional power distribution network. Loss This refers to losses in the power distribution network.

[0063] The unit load adjustment rate constraint is:

[0064] |P i (k) -P i (k-1) |≤V i

[0065] The system spin-off reserve constraint is:

[0066]

[0067] Where P i (k) V represents the output power of the kth turbine of unit i; i R(t) represents the maximum power adjustment in each iteration cycle; R(t) represents the system's spinning reserve capacity at a certain moment.

[0068] An initial incremental cost λ is set for each generator as a unified consistency variable. λ is continuously adjusted until it converges. The converged λ is the incremental cost of each power node under the optimal condition.

[0069] Incremental cost λ and P D The relationship is:

[0070]

[0071] Summarized as follows:

[0072]

[0073] The output P of the i-th distributed power source i It can be calculated using the following formula:

[0074]

[0075] By combining the equality and inequality constraints of distributed economic dispatch, λ and the output of each power source are iteratively updated. When λ converges, the objective function system requirements are met, the power allocation is optimal, and the power generation cost is minimized.

[0076] Furthermore, incremental costs λ and P D The relation can be written as:

[0077] f(λ)=P D +P Loss

[0078] Applying the above equation to λ (k) Point (λ) (k) Expanding the incremental variable (for the k-th round) using a Taylor series and ignoring higher-order terms yields:

[0079]

[0080] Summarized as follows:

[0081]

[0082] Rewritten as:

[0083]

[0084] Where Δλ (k) This is the gradient correction amount, ΔP. (k) The power difference in the k-th iteration is calculated as follows:

[0085]

[0086] Therefore, the update formula for λ is:

[0087] λ (k+1) =λ (k) +Δλ (k)

[0088] Because the load adjustment rate constraint of the generating units limits the rate of change of the output power of each power source, the output power cannot maintain the same rate of change as the incremental variable. That is, the adjustment rate of the incremental variable is higher than the adjustment rate of the output power, causing the output power and the incremental variable to first exceed the optimal value, and then fall back to the optimal value, or even fluctuate multiple times around the optimal value. To suppress this phenomenon and accelerate the convergence speed, a suppression strategy is introduced to limit the adjustment rate of the incremental variable:

[0089]

[0090] Where θ is the adjustment threshold for the incremental variable, x l and x h These are the adjustment coefficients for different thresholds.

[0091] When Δλ (k) When the value is 0, the incremental variables and the output of each distributed power source no longer change, meaning that the distributed economic scheduling has reached the optimal solution.

[0092] (2) Blockchain-based distributed economic scheduling

[0093] Each distributed power source is mapped to a private Ethereum blockchain node, and a private Ethereum blockchain is built using the geth client to achieve distributed economic scheduling. Unlike the public Ethereum blockchain, the private Ethereum blockchain does not involve expensive tokens and has advantages such as fast transaction speed, good real-time performance, low deployment cost, and flexibility, supporting distributed economic scheduling operations. Write permissions are held only by the participants in the economic scheduling process, and data access and recording operations also have strict permission requirements. When initiating scheduling calculations, each node directly obtains the consensus information from the previous round locally, without needing to exchange information with neighboring nodes, effectively preventing individual deception. Simultaneously, the scheduling smart contract is deployed on the private Ethereum blockchain, isolated from the external environment, making the execution process more secure. Information interaction between nodes is encrypted using the blockchain's asymmetric encryption algorithm to ensure system security. The scheduling results of each round are synchronously updated to all nodes through the PoW consensus mechanism, maintaining consistency of scheduling data across all nodes.

[0094] This solution makes the following improvements and adjustments to the Ethereum private blockchain:

[0095] ① Significantly reduces the difficulty of calculating random numbers in the PoW consensus mechanism.

[0096] In the Proof-of-Work (PoW) consensus mechanism, at regular intervals, consensus nodes simultaneously package transactions from the transaction pool. This process requires extensive hash calculations, consuming considerable time and computing power, to obtain a valid random number. The node that first obtains this random number successfully packages a block, gaining the right to record transactions in this consensus. This means the node can broadcast its successfully packaged block, and the remaining nodes verify its correctness. If the verification is successful, consensus is achieved, and the data is uploaded to the blockchain. This strategy requires obtaining the calculation results from the previous round during economic scheduling, thus necessitating rapid on-chain processing of scheduling data for each round. Significantly reducing the difficulty of the PoW consensus mechanism allows for faster random number calculations while ensuring the correctness of the consensus results, thereby improving the speed of uploading scheduling data to the blockchain and guaranteeing the timeliness of the scheduling process. Simultaneously, reducing computational difficulty also lowers the hardware requirements for nodes on the private blockchain, reducing deployment costs.

[0097] ②Increase the gas limit to the maximum allowed.

[0098] Each step of the execution of an Ethereum smart contract requires a certain amount of gas. If the gas is exhausted before the contract execution ends, the contract execution will fail and the data will be rolled back. Increasing the gas limit can effectively avoid this situation.

[0099] The plan addresses individual deceptive behavior in the following ways:

[0100] ① The economic scheduling algorithm used in this scheme employs a globally unified incremental variable to adjust the output of each node. Changes in this incremental variable have the same impact on all nodes. If a deceitful node, in order to increase its own profits, tampers with its local database, thereby increasing the incremental cost, then the output power of all nodes will increase, and the deceitful node will not be able to profit from this.

[0101] ② During the node iterative calculation process, the economic scheduling results of each round are synchronized to all nodes through a consensus mechanism. When starting a new round of calculation, each node retrieves parameters such as power distribution and incremental variables from its local database for iterative calculation. During this process, nodes do not need to exchange information with neighboring nodes temporarily, preventing power nodes from engaging in deceptive behavior. This mechanism adheres to the principle of minimizing information exchange during scheduling while avoiding individual deception issues, thus ensuring the accuracy of economic scheduling.

[0102] The foregoing has shown and described 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. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based economic scheduling method, characterized in that, include: Obtain distributed power sources, build a private Ethereum blockchain network based on the distributed power sources, obtain the output power of the distributed power sources based on the private Ethereum blockchain network, and perform economic scheduling calculations based on the output power of the distributed power sources using a gradient correction algorithm to obtain the economic scheduling results. The process of building an Ethereum private blockchain network includes: The distributed power source is mapped to a blockchain node, and an Ethereum private blockchain network is formed based on the communication lines between the distributed power sources and the blockchain nodes. The upper architecture of the Ethereum private blockchain network is the Ethereum platform layer, and the Ethereum platform layer performs economic scheduling calculations based on smart contracts and consensus algorithms. The process of economic scheduling calculation includes: Construct an objective function, wherein the objective function aims to minimize the total generation cost of distributed power sources; Construct constraints, wherein the constraints include equality constraints and inequality constraints; Based on the objective function, constraints, and the output power of the distributed power source, iterative calculations are performed to obtain the economic dispatch result. The iterative calculation process includes: Based on the output power of the distributed power source, the incremental variable is obtained; The gradient correction amount is obtained by calculating the incremental variable through Taylor series expansion; Based on the constraints and objective function, the incremental variable and the output power of the distributed power source are iteratively updated according to the gradient correction amount. During the iterative update process, the gradient correction amount is adjusted according to the output power of the distributed power source. When the gradient correction amount is zero, the updated incremental variable and the updated output power of the distributed power source are the economic dispatch result. The formula for the gradient correction amount is: in, This is the gradient correction amount. For the first The power difference between rounds of iteration, For each distributed power source, there is a constant term coefficient representing the power generation cost of each power source. .

2. The method according to claim 1, characterized in that: The objective function is the sum of the generation costs of different distributed power sources, and is: in, , For the total cost of electricity generation, For the first The output power of a distributed power source For the first The power generation cost of a distributed power source; , , The first The coefficients of the quadratic, linear, and constant terms of a distributed power source.

3. The method according to claim 1, characterized in that: The equality constraint is: in To meet the current total load demand of the regional power distribution network, This refers to losses in the power distribution network.

4. The method according to claim 1, characterized in that: The inequality constraints include economic dispatch inequality constraints, unit compliance adjustment rate constraints, and system spinning reserve constraints. The economic scheduling inequality constraint is: ; The unit meets the adjustment rate constraint as follows: ; The system rotational reserve constraint is: ; Among them, the for For the first Minimum output power of a distributed power source for For the first The maximum output power of a distributed power source. For the first Unit No. The output power of the wheel; This represents the maximum power adjustment amount for each iteration cycle; The rotating reserve capacity of the system at a certain moment.

5. The method according to claim 1, characterized in that: During the adjustment of the gradient correction amount, the adjustment rate of the gradient correction amount is suppressed by a suppression strategy.

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