Load side power demand response scheduling method and system, electronic equipment and medium

Abstract

The invention provides a load side power demand response scheduling method and system, electronic equipment and a medium, and relates to the technical field of power. The specific implementation scheme is as follows: when a scheduling mechanism node detects that a regional power grid has a load anomaly, a demand response invitation is issued to an aggregator node; and the aggregator node determines a target function according to the industrial load production curve of the regional power grid, the electric vehicle state information, the distributed energy storage information and the electricity price information, and solves the target function to obtain the maximum adjustable capacity. And the scheduling mechanism node determines a subsidy power unit price and an aggregator bid-winning capacity according to an edge clearing mode of price priority, time priority and capacity priority. And the aggregator node obtains the arrangement sequence of each adjustable load node according to the comprehensive scheduling index of each adjustable load node, and schedules each adjustable load in the regional power grid in combination with the load adjustment capacity of each adjustable load node. Therefore, the accuracy and fairness of load scheduling of each node can be ensured.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to a load-side electric power demand response scheduling method, system, electronic equipment and medium. Background Art

[0002] In the new power system, load-side flexible resources such as industrial adjustable loads, electric vehicles, and energy storage are no longer consumers who simply consume electricity, but "prosumers" who both consume and produce electricity, and are called adjustable loads. The behavior of adjustable loads to adjust their operating status or energy demand according to electricity price discounts and incentives to help the power grid achieve a balance between electricity supply and demand is called demand response.

[0003] Industrial controllable loads, electric vehicles, and energy storage are important new entities on the load side. They are characterized by fast response, low cost, and strong controllability, and are important demand response resources. At present, due to objective reasons such as participation thresholds, electricity consumption, and expected returns, the potential of load-side resources to participate in demand response has not been fully released. There is a lack of a flexible, efficient, and transparent unified dispatching mechanism and revenue distribution mechanism. The data of market entities is difficult to trust, the aggregation process is opaque, and the uncertainty of load nodes leads to biased assessments. Summary of the invention

[0004] The present disclosure provides a load-side power demand response scheduling method, system, electronic device and medium.

[0005] According to one aspect of the present disclosure, a load-side power demand response scheduling method is provided, comprising:

[0006] When the dispatching agency node detects abnormal load in the regional power grid, it will issue demand response invitation information to the aggregator node;

[0007] The aggregator node determines an objective function based on the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid in response to the received response invitation information, with the goal of maximizing the comprehensive benefit of the regional power grid;

[0008] The aggregator node solves the objective function based on the constraint conditions of the objective function to obtain the maximum adjustable capacity of the regional power grid, and feeds back the maximum adjustable capacity to the dispatching agency node;

[0009] The dispatching agency node determines the subsidized electricity unit price of the regional power grid and the aggregator's winning bid capacity according to the maximum adjustable capacity fed back by the aggregator node in a marginal clearing manner of price priority, time priority and capacity priority, and feeds back the subsidized electricity unit price and the aggregator's winning bid capacity to the aggregator node;

[0010] The aggregator node decomposes the winning bid capacity of the aggregator according to the smart contract to obtain the load regulation capacity of each adjustable load node in the regional power grid;

[0011] The aggregator node sorts each of the adjustable load nodes according to their comprehensive scheduling indicators to obtain an arrangement order of each of the adjustable load nodes, and based on the arrangement order of each of the adjustable load nodes and the load regulation capacity of each of the adjustable load nodes, performs load scheduling on each of the adjustable load nodes in the regional power grid.

[0012] According to another aspect of the present disclosure, a load-side power demand response scheduling system is provided, comprising:

[0013] The dispatching agency node is used to issue demand response invitation information to the aggregator node when abnormal load is detected in the regional power grid;

[0014] The aggregator node is used to determine an objective function based on the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid in response to the received response invitation information with the goal of maximizing the comprehensive benefit of the regional power grid;

[0015] The aggregator node is further used to solve the objective function based on the constraint conditions of the objective function to obtain the maximum adjustable capacity of the regional power grid, and feed back the maximum adjustable capacity to the dispatching mechanism node;

[0016] The dispatching agency node is further used to determine the subsidized electricity unit price of the regional power grid and the aggregator's winning bid capacity according to the maximum adjustable capacity fed back by the aggregator node in a marginal clearing manner of price priority, time priority and capacity priority, and feed back the subsidized electricity unit price and the aggregator's winning bid capacity to the aggregator node;

[0017] The aggregator node is further used to decompose the winning bid capacity of the aggregator according to the smart contract to obtain the load regulation capacity of each adjustable load node in the regional power grid;

[0018] The aggregator node is also used to sort each of the adjustable load nodes according to their comprehensive scheduling indicators to obtain an arrangement order of each of the adjustable load nodes, and based on the arrangement order of each of the adjustable load nodes and the load regulation capacity of each of the adjustable load nodes, perform load scheduling on each of the adjustable load nodes in the regional power grid.

[0019] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any load-side power demand response scheduling method in the embodiments of the present disclosure.

[0023] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any load-side power demand response scheduling method according to the embodiments of the present disclosure.

[0024] According to the technical solution disclosed in the present invention, when the dispatching agency node detects that the regional power grid has an abnormal load, the dispatching agency node issues a demand response invitation to the aggregator node. The aggregator node determines the objective function according to the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid, and solves the objective function to obtain the maximum adjustable capacity of the regional power grid, and feeds it back to the dispatching agency node. The dispatching agency node determines the subsidized electricity unit price and the winning capacity of the aggregator according to the maximum adjustable capacity of the regional power grid in accordance with the edge clearing method of price priority, time priority and capacity priority, and feeds it back to the aggregator node. The aggregator node sorts each of the adjustable load nodes according to the comprehensive scheduling index of each of the adjustable load nodes, obtains the arrangement order of each of the adjustable load nodes, and based on the arrangement order of each of the adjustable load nodes, and the load adjustment capacity of each of the adjustable load nodes obtained based on the subsidized electricity unit price and the winning capacity of the aggregator, load dispatches each of the adjustable load nodes in the regional power grid. In this way, the accuracy and fairness of the load dispatch of each node can be guaranteed.

[0025] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0027] Figure 1 is a flow chart of a load-side power demand response scheduling method according to an embodiment of the present disclosure;

[0028] Figure 2 is a structural block diagram of a load-side power demand response dispatching system according to an embodiment of the present disclosure;

[0029] Figure 3 is an architectural block diagram of a load-side power demand response dispatching system according to an embodiment of the present disclosure;

[0030] Figure 4 The dispatching process of the load-side power demand response dispatching system according to an embodiment of the present disclosure is

[0031] Figure 5 It is a block diagram of an electronic device used to implement the load-side power demand response scheduling method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0033] Explanation of relevant terms

[0034] Demand response: When wholesale electricity market prices rise or system reliability is threatened, electricity users, after receiving direct compensation notices from power suppliers to induce load reduction or signals of rising electricity prices, change their inherent habitual electricity consumption patterns to reduce or shift electricity loads during a certain period of time in response to electricity supply, thereby ensuring grid stability and curbing short-term behavior of rising electricity prices.

[0035] Blockchain: Blockchain is a decentralized distributed ledger that is tamper-proof, secure and reliable. It combines distributed storage, peer-to-peer transmission, consensus mechanism, cryptography and other technologies to record transactions and information through data blocks to ensure data security and transparency.

[0036] Digital signature: A string of numbers that can only be generated by the sender of the information and cannot be forged by others. This string of numbers is an effective proof of the authenticity of the information sent by the sender. Digital signature is the application of asymmetric key encryption technology and digital summary technology.

[0037] Analytical Hierarchy Process (AHP): A subjective weighting method that breaks down elements related to decision-making into levels such as goals, criteria, and plans, and conducts qualitative and quantitative analysis on this basis.

[0038] Entropy weight method: an objective weighting method that determines the indicator weight based on the amount of information contained in each indicator value.

[0039] Figure 1It is a flow chart of a load-side power demand response scheduling method according to an embodiment of the present disclosure.

[0040] like Figure 1 As shown, the load-side power demand response scheduling method includes:

[0041] S110, when the dispatching agency node detects that the regional power grid has an abnormal load, it issues a demand response invitation message to the aggregator node;

[0042] S120, the aggregator node determines an objective function based on the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid in response to the received response invitation information, with the goal of maximizing the comprehensive benefits of the regional power grid;

[0043] S130, the aggregator node solves the objective function based on the constraint conditions of the objective function, obtains the maximum adjustable capacity of the regional power grid, and feeds back the maximum adjustable capacity to the dispatching agency node;

[0044] S140, the dispatching agency node determines the subsidized electricity unit price of the regional power grid and the aggregator's winning bid capacity according to the maximum adjustable capacity fed back by the aggregator node, in accordance with the marginal clearing method of price priority, time priority and capacity priority, and feeds back the subsidized electricity unit price and the aggregator's winning bid capacity to the aggregator node;

[0045] S150, the aggregator node decomposes the aggregator's winning bid capacity according to the smart contract to obtain the load regulation capacity of each adjustable load node in the regional power grid;

[0046] S160, the aggregator node sorts each adjustable load node according to the comprehensive dispatching index of each adjustable load node to obtain the arrangement order of each adjustable load node, and based on the arrangement order of each adjustable load node and the load regulation capacity of each adjustable load node, performs load dispatch on each adjustable load node in the regional power grid.

[0047] It can be understood that the information or data obtained in each of the above steps can be signed, encrypted, uploaded to the blockchain and stored, so that the data can be securely stored and traced.

[0048] For example, an aggregated demand response alliance chain system can be pre-deployed. The alliance chain consists of multiple alliance nodes. Considering the principle of matching rights and responsibilities, aggregators, dispatching agencies and power trading centers are used as consensus nodes, assume core business responsibilities, and participate in the consensus mechanism of the alliance chain. The power dispatching agency acts as a storage node to store and supervise data to ensure that it is credible and controllable. Distributed power users and electric vehicle users select consensus nodes through the VRF (Verifiable Random Function) algorithm.

[0049] For example, the consortium chain can adopt a semi-distributed topology structure. In this way, while ensuring fault tolerance, the system consensus efficiency is guaranteed while minimizing the communication complexity and communication hops.

[0050] Among them, the semi-distributed topology structure can refer to a topology structure of a P2P network, where consensus nodes form a high-speed forwarding layer, and consensus nodes and light nodes form several layers. Under the premise of ensuring fault tolerance, the communication complexity and communication hop count can be minimized.

[0051] For example, the alliance chain can adopt a hybrid consensus algorithm of DPOS+PBFT to decouple the two links of block generation and block finalization, ensuring stable consensus performance.

[0052] Among them, the hybrid consensus algorithm of DPOS+PBFT adopts DPOS consensus in the block generation stage and PBFT consensus in the block finalization stage. When the master node of PBFT consensus fails, it will not affect the block generation of DPOS consensus, and PBFT consensus can finalize the block in a timely and certain manner, ensuring a shorter transaction confirmation delay.

[0053] Exemplarily, the abnormal load situation in the regional power grid may include insufficient backup capacity of the power system of the regional power grid, local load overload, excessive peak-to-valley difference in the power grid, etc.

[0054] Exemplarily, the demand response invitation information may include a load baseline, a plurality of response periods, and a load capacity gap.

[0055] For example, the dispatching agency node can determine the unit startup combination based on the load forecast curve before the day in the regional power grid, the unit maintenance plan, the system spare capacity and boundary conditions. When the power system has a large peak-to-valley difference, insufficient spare capacity or local load overload, the power dispatching agency node proposes a peak-cutting demand response start request. When the load level is low and the differential adjustment capacity is insufficient, a valley-filling demand response start request is proposed.

[0056] For example, the electric vehicle status information may include a user's desired charge level, the battery capacity of the electric vehicle, the initial charge, and the charge and discharge efficiency.

[0057] Exemplarily, the distributed energy storage information may include the state of charge, charge and discharge power, and charge and discharge efficiency of the batteries of each energy storage node in each time period, as well as the rated capacity and self-discharge rate of the batteries of each energy storage node.

[0058] Exemplarily, by using a particle swarm algorithm, the objective function is solved under the premise of satisfying the constraint conditions of the objective function, and the maximum adjustable capacity of the regional power grid can be obtained.

[0059] Exemplarily, after the dispatching agency node calculates the unit price of electricity subsidy and the winning bid capacity of the aggregator, the information can be fed back to the aggregator node so that the aggregator node accepts the invitation. After the aggregator node accepts the invitation, the aggregator node can generate a demand response electronic contract based on the unit price of electricity subsidy and the winning bid capacity of the aggregator, and the electronic contract is stored on the chain after being broadcast by the node. For example, the electronic contract module can be called to generate the electronic contract.

[0060] Exemplarily, the electronic contract module includes a data storage module, a digital signature module, and a contract reading module. The data storage module is used to write information such as the subsidized electricity unit price and the aggregator's winning bid capacity into the electronic contract through a smart contract, and provides an unalterable data storage function through the blockchain. The digital signature module is used to sign the demand response electronic contract. When the dispatching agency node, the aggregator node, and the adjustable load node have all signed the contract, the electronic contract is valid. The contract reading module is used to read the electronic contract.

[0061] Exemplarily, the aggregator node writes the subsidized electricity unit price and the aggregator's winning capacity into the electronic contract, stores the electronic contract on the chain through the data storage module, and the dispatching agency node, distributed load node, and aggregator node sign the contract through the digital signature module.

[0062] For example, the on-chain process may include: the master node is selected according to the rules, and the other nodes are slave nodes; the master node packages the invitation content into blocks and broadcasts it to the entire network, and the other nodes are responsible for receiving the broadcasted block information and verifying the correctness of the generated blocks. If more than 2 / 3 of the nodes agree, the block is finalized.

[0063] For example, the smart contract records the ratio between the total adjustable capacity of the aggregator and the adjustable capacity of each adjustable load node. Therefore, the winning capacity of the aggregator can be decomposed according to the smart contract to obtain the load adjustment capacity of each adjustable load node.

[0064] Exemplarily, the comprehensive dispatch index of each adjustable load node can be calculated in advance.

[0065] According to the above implementation, when the dispatching agency node detects that the regional power grid has an abnormal load, the dispatching agency node issues a demand response invitation to the aggregator node. The aggregator node determines the objective function based on the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid, and solves the objective function to obtain the maximum adjustable capacity of the regional power grid, and feeds it back to the dispatching agency node. The dispatching agency node determines the subsidized electricity unit price and the winning capacity of the aggregator according to the maximum adjustable capacity of the regional power grid in accordance with the edge clearing method of price priority, time priority and capacity priority, and feeds it back to the aggregator node. The aggregator node sorts each of the adjustable load nodes according to the comprehensive scheduling index of each of the adjustable load nodes, obtains the arrangement order of each of the adjustable load nodes, and based on the arrangement order of each of the adjustable load nodes, and the load adjustment capacity of each of the adjustable load nodes obtained based on the subsidized electricity unit price and the winning capacity of the aggregator, load dispatches each of the adjustable load nodes in the regional power grid. In this way, the accuracy and fairness of the load dispatch of each node can be guaranteed.

[0066] In one embodiment, the adjustable load points include electric vehicle nodes, industrial load nodes and energy storage nodes, and the objective function is:

[0067]

[0068] Among them, F represents the comprehensive benefits of the regional power grid, P sub,t represents the power of the aggregator’s response at time t, C bid,t represents the compensation load electricity price at time t, ch(t) represents the base electricity price at time t, represents the power of the kth industrial load node at time t, c represents the industrial load output cost coefficient, represents the charging and discharging power of the kth energy storage node at time t, η represents the energy storage usage cost, represents the response power of the kth electric vehicle at time t, and λ represents the response cost coefficient of the electric vehicle.

[0069] In one embodiment, the constraints of the objective function include electric vehicle charging and discharging power limit constraints, electric vehicle charging and discharging capacity limit constraints, industrial load output limit constraints, energy storage output limit constraints, interconnection line capacity constraints, main grid constraints, and power balance constraints between the main grid and each adjustable load node.

[0070] For example, the electric vehicle charging and discharging power limit constraint can be expressed by the following formula:

[0071]

[0072] in, represents the charging and discharging power of the kth electric vehicle at time t, is the lower limit of charging and discharging power of electric vehicles, It is the upper limit of charging and discharging power of electric vehicles.

[0073] For example, the industrial load output limit constraint can be expressed by the following formula:

[0074]

[0075] in, Indicates the lower limit of the charging and discharging power of the industrial load node. Indicates the upper limit of the charging and discharging power of the industrial load node.

[0076] For example, the energy storage battery charging and discharging power limit constraint can be expressed by the following formula:

[0077]

[0078] in, represents the charging and discharging power of the kth energy storage battery at time t, is the lower limit of the charging and discharging power of the energy storage battery, It is the upper limit of the charging and discharging power of the energy storage battery.

[0079] For example, the electric vehicle battery charge and discharge capacity limit constraint can be expressed by the following formula:

[0080]

[0081] represents the battery capacity of the electric vehicle at time t+1, represents the battery capacity of the electric vehicle at time t, η evc Represents the battery charging efficiency, η evd Indicates the battery discharge efficiency, Indicates the lower limit of electric vehicle battery capacity, represents the upper limit of the battery capacity of the electric vehicle, and Δt represents the time difference between time t+1 and time t.

[0082] For example, the energy storage battery charge and discharge capacity limit constraint can be expressed by the following formula:

[0083]

[0084] in, represents the battery capacity at the energy storage time t+1, represents the battery capacity at time t, η evc Represents the battery charging efficiency, η evd Indicates the battery discharge efficiency, Indicates the lower limit of energy storage battery capacity. represents the upper limit of the energy storage battery capacity, and Δt represents the time difference between time t+1 and time t.

[0085] For example, the tie line capacity constraint may be expressed by the following formula:

[0086] S feader(t) ≤S maxfeader

[0087]

[0088] Among them, S feader(t) represents the capacity of the interconnection line at time t, S maxfeader represents the upper limit of the tie line capacity at time t, P t represents the total power of the aggregator response at time t, Represents the tie line loss at time t.

[0089] For example, the main network constraint can be expressed by the following formula:

[0090] P t ≤P max

[0091] Among them, P max Indicates the upper limit of the main network power.

[0092] The power balance constraint can be expressed by the following formula:

[0093]

[0094] According to the above embodiments, the constraint conditions of the objective function can be determined.

[0095] In one embodiment, it also includes: the aggregator node calculates the scheduling success rate, response delay rate, cost loss and demand response potential of each adjustable load node respectively; the aggregator node inputs the scheduling success rate, response delay rate, cost loss and demand response potential of the adjustable load node into the comprehensive scheduling index calculation model to obtain the comprehensive scheduling index of the adjustable load node output by the comprehensive scheduling index calculation model.

[0096] It can be understood that the comprehensive scheduling index calculation model is a pre-trained model.

[0097] Exemplarily, the comprehensive scheduling index calculation model may include an input layer, a hidden layer, and an output layer. The input layer and the hidden layer are long short-term memory neural network (LSTM) layers with 4 and 10 nodes respectively. The output layer is a fully connected layer with 1 node.

[0098] During training, you can use the Adam optimizer. To reduce overfitting, you can introduce a Dropout layer after the two LSTM layers with a Dropout rate of 0.2. By randomly discarding some neurons, you can reduce the network's dependence on specific data samples during training.

[0099] During training, you can set 2000 training cycles (epochs) and the batch size (batch size) to 1. During the training process, check the model accuracy. If the relative error is less than the set threshold or reaches the maximum number of iterations, the training is terminated.

[0100] Exemplarily, the long short-term memory neural network includes a forget gate, a state update unit, and an output gate.

[0101] According to the above implementation, the neural network model can be used to calculate the comprehensive dispatch index of each adjustable load node, which is convenient for the subsequent sorting of each adjustable load node.

[0102] In one embodiment, the scheduling success rate, response delay rate, cost loss and demand response potential of an adjustable load node are calculated, including: determining the scheduling success rate of the adjustable load node based on the ratio between the historical number of completed response scheduling times of the adjustable load node and the historical total scheduling times of the adjustable load node; determining the response delay rate of the adjustable load node based on the ratio between the sum of the durations of each response scheduling of the adjustable load node exceeding the allowable range and the sum of the total durations of each response scheduling; determining the cost loss of the adjustable load node based on the product of the average unit charge and discharge loss cost of the adjustable load node and the dispatchable capacity of the adjustable load node; determining the demand response potential of the adjustable load node based on the difference between the typical daily maximum load and the typical daily minimum load of the adjustable load node and the ratio between the typical daily maximum load.

[0103] According to the above implementation, the scheduling success rate, response delay rate, cost loss and demand response potential of the adjustable load node can be accurately calculated.

[0104] In one embodiment, the method further includes: the aggregator node constructs a basic indicator matrix based on the basic indicator samples for each adjustable load node, wherein each element in the basic indicator matrix is ​​used to represent one of the scheduling success rate, response delay rate, cost loss and demand response potential in the basic indicator sample of an adjustable load node; the aggregator node analyzes the basic indicator matrix based on a hierarchical analysis model to obtain the subjective indicator weight corresponding to each element in the basic indicator matrix; the aggregator node analyzes the basic indicator matrix based on an entropy weight model to obtain the objective indicator weight corresponding to each element in the basic indicator matrix; the aggregator node The observation indicator weight and the objective indicator weight are used to determine the combination weight corresponding to each element in the basic indicator matrix; the aggregator node performs weighted summation on the four elements in the basic indicator matrix that belong to the same basic indicator sample, and the combination weights corresponding to each of the four elements, to obtain the comprehensive scheduling indicator sample corresponding to the basic indicator sample; the aggregator node constructs a training sample set based on each basic indicator sample and its corresponding comprehensive scheduling indicator sample, wherein each training sample in the training sample set includes a feature vector composed of the basic indicator sample and a target feature composed of the comprehensive scheduling indicator sample corresponding to the basic indicator sample; the aggregator node trains the comprehensive scheduling indicator calculation model based on the training sample set.

[0105] For example, the basic indicator matrix can be expressed as follows:

[0106]

[0107] Among them, D km It is the mth basic indicator of the kth adjustable load node, for example, one of the scheduling success rate, response delay rate, cost loss and demand response potential.

[0108] For example, for each element in the basic indicator matrix, the subjective indicator weight corresponding to and objective indicator weights The combined weight corresponding to each element in the basic indicator matrix can be expressed as follows:

[0109]

[0110] Exemplarily, the comprehensive scheduling index sample corresponding to the basic index sample may be:

[0111]

[0112] In this way, the comprehensive scheduling indicator samples corresponding to each basic indicator sample can be obtained, thereby constructing a training sample set.

[0113] For example, after the model completes the calculation of the comprehensive scheduling index for a specified number of times, the training sample set is adaptively updated. Then, the newly generated data is added to the training sample set, and the model is retrained to achieve adaptive updating of the model parameters.

[0114] According to the above implementation, the model can be trained using the above training sample set.

[0115] Figure 2 It is a structural block diagram of a load-side power demand response scheduling system according to an embodiment of the present disclosure.

[0116] like Figure 2 As shown, the load-side power demand response dispatching system may include a consortium chain, which includes a dispatching agency node, an aggregator node and multiple adjustable load nodes.

[0117] The dispatching agency node is used to issue demand response invitation information to the aggregator node when abnormal load is detected in the regional power grid;

[0118] The aggregator node is used to determine an objective function based on the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid in response to the received response invitation information with the goal of maximizing the comprehensive benefit of the regional power grid;

[0119] The aggregator node is further used to solve the objective function based on the constraint conditions of the objective function to obtain the maximum adjustable capacity of the regional power grid, and feed back the maximum adjustable capacity to the dispatching mechanism node;

[0120] The dispatching agency node is further used to determine the subsidized electricity unit price of the regional power grid and the aggregator's winning bid capacity according to the maximum adjustable capacity fed back by the aggregator node in a marginal clearing manner of price priority, time priority and capacity priority, and feed back the subsidized electricity unit price and the aggregator's winning bid capacity to the aggregator node;

[0121] The aggregator node is further used to decompose the winning bid capacity of the aggregator according to the smart contract to obtain the load regulation capacity of each adjustable load node in the regional power grid;

[0122] The aggregator node is also used to sort each of the adjustable load nodes according to their comprehensive scheduling indicators to obtain an arrangement order of each of the adjustable load nodes, and based on the arrangement order of each of the adjustable load nodes and the load regulation capacity of each of the adjustable load nodes, perform load scheduling on each of the adjustable load nodes in the regional power grid.

[0123] For example, the architecture diagram of the load-side power demand response dispatching system can be as follows: Figure 3 shown.

[0124] For example, the dispatching process of the load-side power demand response dispatching system can be as follows: Figure 4 shown.

[0125] In one embodiment, the adjustable load points include electric vehicle nodes, industrial load nodes and energy storage nodes, and the objective function is:

[0126]

[0127] Among them, F represents the comprehensive benefits of the regional power grid, P sub,t represents the power of the aggregator’s response at time t, C bid,t represents the compensation load electricity price at time t, ch(t) represents the base electricity price at time t, represents the power of the kth industrial load node at time t, c represents the industrial load output cost coefficient, represents the charging and discharging power of the kth energy storage node at time t, η represents the energy storage usage cost, represents the response power of the kth electric vehicle at time t, and λ represents the response cost coefficient of the electric vehicle.

[0128] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0129] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0130] Figure 5 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0131] like Figure 5As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0132] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0133] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the load-side power demand response scheduling method. For example, in some embodiments, the load-side power demand response scheduling method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the load-side power demand response scheduling method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the load-side power demand response scheduling method in any other appropriate manner (for example, by means of firmware).

[0134] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0136] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 of the foregoing.

[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0138] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0139] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0140] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0141] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A load-side power demand response dispatching method, comprising: When the dispatching agency node detects abnormal load in the regional power grid, it will issue demand response invitation information to the aggregator node; The aggregator node determines an objective function based on the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid in response to the received response invitation information, with the goal of maximizing the comprehensive benefit of the regional power grid; The aggregator node solves the objective function based on the constraint conditions of the objective function to obtain the maximum adjustable capacity of the regional power grid, and feeds back the maximum adjustable capacity to the dispatching agency node; The dispatching agency node determines the subsidized electricity unit price of the regional power grid and the aggregator's winning bid capacity according to the maximum adjustable capacity fed back by the aggregator node in a marginal clearing manner of price priority, time priority and capacity priority, and feeds back the subsidized electricity unit price and the aggregator's winning bid capacity to the aggregator node; The aggregator node decomposes the winning bid capacity of the aggregator according to the smart contract to obtain the load regulation capacity of each adjustable load node in the regional power grid; The aggregator node sorts each of the adjustable load nodes according to their comprehensive scheduling indicators to obtain an arrangement order of each of the adjustable load nodes, and based on the arrangement order of each of the adjustable load nodes and the load regulation capacity of each of the adjustable load nodes, performs load scheduling on each of the adjustable load nodes in the regional power grid.

2. The method according to claim 1, characterized in that: The adjustable load points include electric vehicle nodes, industrial load nodes and energy storage nodes, and the objective function is: Among them, F represents the comprehensive benefits of the regional power grid, P sub,t represents the power of the aggregator’s response at time t, C bid,t represents the compensation load electricity price at time t, ch(t) represents the base electricity price at time t, represents the power of the kth industrial load node at time t, c represents the industrial load output cost coefficient, represents the charging and discharging power of the kth energy storage node at time t, η represents the energy storage usage cost, represents the response power of the kth electric vehicle at time t, and λ represents the response cost coefficient of the electric vehicle.

3. The method according to claim 2, characterized in that The constraints of the objective function include electric vehicle charging and discharging power limit constraints, electric vehicle charging and discharging capacity limit constraints, industrial load output limit constraints, energy storage output limit constraints, interconnection line capacity constraints, main grid constraints, and power balance constraints between the main grid and each adjustable load node.

4. The method according to claim 1, characterized in that: Also includes: The aggregator node calculates the scheduling success rate, response delay rate, cost loss and demand response potential of each adjustable load node respectively; The aggregator node inputs the scheduling success rate, response delay rate, cost loss and demand response potential of the adjustable load node into the comprehensive scheduling index calculation model to obtain the comprehensive scheduling index of the adjustable load node output by the comprehensive scheduling index calculation model.

5. The method according to claim 4, characterized in that The calculating of the dispatch success rate, response delay rate, cost loss and demand response potential of the adjustable load node includes: Determining a scheduling success rate of the adjustable load node based on a ratio between the number of historical completed response schedulings of the adjustable load node and the total number of historical schedulings of the adjustable load node; Determine the response delay rate of the adjustable load node based on the ratio between the sum of the durations of each response scheduling of the adjustable load node exceeding the allowable range and the sum of the total durations of each response scheduling; Determine the cost loss of the adjustable load node based on the product of the average unit charge and discharge loss cost of the adjustable load node and the dispatchable capacity of the adjustable load node; The demand response potential of the adjustable load node is determined based on the ratio of the difference between the typical day maximum load and the typical day minimum load of the adjustable load node to the typical day maximum load.

6. The method according to claim 1, characterized in that Also includes: The aggregator node constructs a basic indicator matrix based on the basic indicator samples for each of the adjustable load nodes, wherein each element in the basic indicator matrix is ​​used to represent one of the scheduling success rate, response delay rate, cost loss and demand response potential in the basic indicator sample of one of the adjustable load nodes; The aggregator node analyzes the basic indicator matrix based on a hierarchical analysis model to obtain a subjective indicator weight corresponding to each element in the basic indicator matrix; The aggregator node analyzes the basic indicator matrix based on the entropy weight model to obtain the objective indicator weight corresponding to each element in the basic indicator matrix; The aggregator node determines the combined weight corresponding to each element in the basic indicator matrix based on the subjective indicator weight and the objective indicator weight corresponding to each element in the basic indicator matrix; The aggregator node performs weighted summation on four elements in the basic indicator matrix that belong to the same basic indicator sample, and the combined weights corresponding to each of the four elements, to obtain a comprehensive scheduling indicator sample corresponding to the basic indicator sample; The aggregator node constructs a training sample set based on each of the basic indicator samples and the corresponding comprehensive scheduling indicator samples, wherein each training sample in the training sample set includes a feature vector composed of the basic indicator sample and a target feature composed of the comprehensive scheduling indicator sample corresponding to the basic indicator sample; The aggregator node trains the comprehensive scheduling indicator calculation model based on the training sample set.

7. A load-side power demand response dispatching system, comprising: The dispatching agency node is used to issue demand response invitation information to the aggregator node when abnormal load is detected in the regional power grid; The aggregator node is used to determine an objective function based on the industrial load production curve, electric vehicle status information, distributed energy storage information and electricity price information of the regional power grid in response to the received response invitation information with the goal of maximizing the comprehensive benefit of the regional power grid; The aggregator node is further used to solve the objective function based on the constraint conditions of the objective function to obtain the maximum adjustable capacity of the regional power grid, and feed back the maximum adjustable capacity to the dispatching mechanism node; The dispatching agency node is further used to determine the subsidized electricity unit price of the regional power grid and the aggregator's winning bid capacity according to the maximum adjustable capacity fed back by the aggregator node in a marginal clearing manner of price priority, time priority and capacity priority, and feed back the subsidized electricity unit price and the aggregator's winning bid capacity to the aggregator node; The aggregator node is further used to decompose the winning bid capacity of the aggregator according to the smart contract to obtain the load regulation capacity of each adjustable load node in the regional power grid; The aggregator node is also used to sort each of the adjustable load nodes according to their comprehensive scheduling indicators to obtain an arrangement order of each of the adjustable load nodes, and based on the arrangement order of each of the adjustable load nodes and the load regulation capacity of each of the adjustable load nodes, perform load scheduling on each of the adjustable load nodes in the regional power grid.

8. The system according to claim 7, characterized in that The adjustable load points include electric vehicle nodes, industrial load nodes and energy storage nodes, and the objective function is: Among them, F represents the comprehensive benefits of the regional power grid, P sub,t represents the power of the aggregator’s response at time t, C bid,t represents the compensation load electricity price at time t, ch(t) represents the base electricity price at time t, represents the power of the kth industrial load node at time t, c represents the industrial load output cost coefficient, represents the charging and discharging power of the kth energy storage node at time t, η represents the energy storage usage cost, represents the response power of the kth electric vehicle at time t, and λ represents the response cost coefficient of the electric vehicle.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

Images