CN110659827B - Energy scheduling method, child node system, scheduling system, and storage medium - Google Patents
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Abstract
The utility model provides an energy scheduling method, a sub-node system, a scheduling system and a storage medium, which relate to the technical field of energy transaction, wherein the method comprises the following steps: the energy sub-node system broadcasts the existing energy state and the expected energy state of the energy sub-node system, and generates a scheduling strategy based on the existing energy state and the expected energy state of the energy sub-node system and the received other energy sub-node systems; and determining an optimal scheduling strategy based on a preset negotiation arbitration rule, broadcasting the optimal scheduling strategy in the regional energy scheduling system, and performing corresponding scheduling and/or transaction processing by the energy sub-node system based on the optimal scheduling strategy. The method, the sub-node system, the scheduling system and the storage medium can improve the accuracy of the scheduling strategy, determine the optimal scheduling strategy by adopting a negotiation arbitration mode, improve the flexibility of regional energy scheduling, avoid malicious nodes from tampering block chain data, improve the safety and improve the user experience.
Description
Technical Field
The present disclosure relates to the field of energy transaction technologies, and in particular, to an energy scheduling method, an energy sub-node system, a regional energy scheduling system, and a storage medium.
Background
The energy internet is a novel ecological energy system, and is an energy network which takes an electric power system as a core and has traditional independent operation of electricity, heat, gas, oil, traffic and the like, and forms a novel energy collaborative optimization and intercommunication interconnection network by taking an advanced information communication technology and an energy trading system as links. At present, when dispatching and trading are carried out on generating electric power, water, gas, heat and the like, a dispatching strategy is generated only based on the existing energy state, and the accuracy of the dispatching strategy is not high; in addition, in the existing regional energy scheduling system, a centralized scheduling strategy decision mode is adopted, a scheduling strategy is generated through one node, energy scheduling and transaction cannot be performed if the node fails, and the node is large in workload and prone to failure.
Disclosure of Invention
In view of the above, a technical problem to be solved by the present disclosure is to provide an energy scheduling method, an energy sub-node system, a regional energy scheduling system, and a storage medium, which can generate a scheduling policy by combining the existing energy states and expected energy states of itself and other energy sub-node systems, and determine an optimal scheduling policy by means of negotiation arbitration.
According to an aspect of the present disclosure, there is provided an energy scheduling method applied to a regional energy scheduling system, the regional energy scheduling system including: a plurality of energy sub-node systems; the method comprises the following steps: the energy sub-node system broadcasts the existing energy state and the expected energy state of the energy sub-node system in the regional energy dispatching system; the energy sub-node system generates a scheduling strategy based on the current energy state and the expected energy state of the energy sub-node system and the received current energy state and expected energy state of other energy sub-node systems; all the energy sub-node systems determine an optimal scheduling strategy corresponding to the regional energy scheduling system based on a preset negotiation arbitration rule, and broadcast the optimal scheduling strategy in the regional energy scheduling system; and the energy sub-node system carries out corresponding scheduling and/or transaction processing based on the optimal scheduling strategy.
Optionally, the determining, by all the energy sub-node systems, an optimal scheduling policy corresponding to the regional energy scheduling system based on a preset negotiation arbitration rule, and broadcasting the optimal scheduling policy in the regional energy scheduling system includes: at least one energy sub-node system in all the energy sub-node systems broadcasts a scheduling strategy of the energy sub-node system in the regional energy scheduling system; and if the energy sub-node system determines that the scheduling strategy of the energy sub-node system is superior to the received scheduling strategies of all other energy sub-node systems, determining the scheduling strategy of the energy sub-node system as the optimal scheduling strategy and broadcasting the optimal scheduling strategy in the regional energy scheduling system.
Optionally, the broadcasting, by at least one energy sub-node system of all the energy sub-node systems, its own scheduling policy in the regional energy scheduling system includes: step one, all the energy sub-node systems are in an activated state, and one energy sub-node system in the activated state broadcasts a scheduling strategy in the regional energy scheduling system; step two, judging whether the self scheduling strategy is superior to the received scheduling strategy by other energy sub-node systems in the activated state, and if not, setting the self in the deactivated state; step three, if the number of the energy sub-node systems in the activated state is more than 1, determining that the next energy sub-node system in the activated state broadcasts a scheduling strategy in the regional energy scheduling system; and repeating the second step and the third step until only one energy sub-node system in the activated state exists.
Optionally, if the energy sub-node system is in an active state and has a token, the energy sub-node system broadcasts its own scheduling policy in the regional energy scheduling system, and passes the token to the next energy sub-node system in the active state.
Optionally, if there is only one energy sub-node system in an active state, the energy sub-node system determines its own scheduling policy as the optimal scheduling policy, and broadcasts a scheduling policy receiving message in the regional energy scheduling system, so as to notify all energy sub-node systems in an inactive state to receive the optimal scheduling policy.
Optionally, the energy sub-node system establishes a prediction model, and trains the prediction model; and the energy sub-node system predicts the existing energy state by using the trained prediction model to obtain the expected energy state.
Optionally, the training the prediction model comprises: the energy sub-node system obtains a historical energy detection signal and carries out first stability detection on the historical energy detection signal; if the first stability detection is determined to pass, the energy sub-node system decomposes the historical energy detection signal to obtain a plurality of first sub-signals; the energy sub-node system classifies the plurality of first sub-signals to obtain a first high-frequency sub-signal and a first low-frequency sub-signal; and the energy sub-node system respectively trains a first predictor model and a second predictor model by using the first high-frequency sub-signal and the first low-frequency sub-signal.
Optionally, the energy sub-node system predicts the existing energy state by using the trained prediction model, and obtaining the expected energy state includes: the energy sub-node system obtains an existing energy detection signal and carries out second stability detection on the existing energy detection signal; if the second stability detection is determined to pass, the energy sub-node system decomposes the existing energy detection signal to obtain a plurality of second sub-signals; the energy sub-node system classifies the plurality of second sub-signals to obtain a second high-frequency sub-signal and a second low-frequency sub-signal; the energy sub-node system respectively inputs the second high-frequency sub-signal and the second low-frequency sub-signal into the first predictor model and the second predictor model to obtain a first predictor result and a second predictor result; the energy sub-node system generates the expected energy state based on the first predictor result and the second predictor result.
Optionally, the first predictor model comprises: a Markov chain model; the second predictor model includes: autoregressive moving average model.
Optionally, after the scheduling processing and/or the energy transaction corresponding to the regional energy scheduling system is finished, each energy sub-node system writes the scheduling processing information and/or the energy transaction information into its respective block chain.
Optionally, the writing, by each energy sub-node system, the scheduling processing information and/or the energy transaction information into each blockchain includes: and if the energy sub-node system is a credible node and is granted with the recording authority, generating a data block corresponding to scheduling processing information and/or energy transaction information, and broadcasting the data block to other energy sub-node systems, so that the energy sub-node system as a credible node and the other energy sub-node systems respectively add the data block to the tail of each block chain.
Optionally, the energy sources corresponding to the existing energy source state and the expected energy source state comprise: at least one of electric energy, heat energy, water and gas.
According to another aspect of the present disclosure, there is provided an energy sub-node system in a regional energy scheduling system, including: the energy state prediction module is used for broadcasting the existing energy state and the expected energy state of the energy sub-node system to which the energy sub-node system belongs in the regional energy scheduling system; the scheduling strategy processing module is used for generating a scheduling strategy based on the existing energy state and the expected energy state of the energy sub-node system to which the scheduling strategy belongs and the received existing energy state and the expected energy state of other energy sub-node systems; the scheduling processing module is used for carrying out corresponding scheduling and/or transaction processing based on the optimal scheduling strategy; and all energy sub-node systems in the regional energy scheduling system determine an optimal scheduling strategy corresponding to the regional energy scheduling system based on a preset negotiation arbitration rule, and broadcast the optimal scheduling strategy in the regional energy scheduling system.
Optionally, at least one energy sub-node system of all the energy sub-node systems broadcasts its own scheduling policy in the regional energy scheduling system, wherein the scheduling policy processing module is configured to determine the scheduling policy of the energy sub-node system to which it belongs as the optimal scheduling policy and broadcast it in the regional energy scheduling system if it is determined that the scheduling policy of the energy sub-node system to which it belongs is better than the received scheduling policies of all other energy sub-node systems.
Optionally, the scheduling policy processing module is further configured to, if it is determined that the energy sub-node system to which the energy sub-node system belongs is in an activated state and has a token, broadcast the scheduling policy of the energy sub-node system to which the energy sub-node system belongs in the regional energy scheduling system, and transmit the token to the energy sub-node system in the next activated state, where the energy sub-node system in the activated state determines whether the scheduling policy of the energy sub-node system is better than the received scheduling policy, and if not, sets the energy sub-node system in the deactivated state.
Optionally, the scheduling policy processing module is further configured to determine, if it is determined that only the energy sub-node system to which the scheduling policy processing module belongs is in an activated state, the scheduling policy of the energy sub-node system to which the scheduling policy processing module belongs as the optimal scheduling policy, and broadcast a scheduling policy receiving message in the regional energy scheduling system, so as to notify all the energy sub-node systems in an inactivated state to receive the optimal scheduling policy.
Optionally, the energy state prediction module includes: the model training unit is used for establishing a prediction model and training the prediction model; and the state prediction unit is used for predicting the existing energy state by using the trained prediction model to obtain the expected energy state.
Optionally, the model training unit is further configured to obtain a historical energy detection signal, and perform a first stability detection on the historical energy detection signal; if the first stability detection is determined to pass, decomposing the historical energy detection signal to obtain a plurality of first sub-signals; classifying the plurality of first sub-signals to obtain a first high-frequency sub-signal and a first low-frequency sub-signal; and respectively training a first predictor model and a second predictor model by using the first high-frequency sub-signal and the first low-frequency sub-signal.
Optionally, the state prediction unit is further configured to obtain an existing energy detection signal, and perform a second stability detection on the existing energy detection signal; if the second stability detection is determined to pass, decomposing the existing energy detection signal to obtain a plurality of second sub-signals; classifying the plurality of second sub-signals to obtain a second high-frequency sub-signal and a second low-frequency sub-signal; inputting the second high-frequency sub-signal and the second low-frequency sub-signal into the first predictor model and the second predictor model respectively to obtain a first predictor result and a second predictor result; generating the expected energy state based on the first predictor result and the second predictor result.
Optionally, the energy blockchain module is configured to write scheduling processing information and/or energy transaction information into a blockchain of an energy sub-node system to which the energy blockchain module belongs after scheduling processing and/or energy transaction corresponding to the regional energy scheduling system is completed.
Optionally, the blockchain module is further configured to, if it is determined that the energy sub-node system to which the blockchain module belongs is a trusted node and is granted a recording authority, generate a data block corresponding to the scheduling processing information and/or the energy transaction information, and broadcast the data block to other energy sub-node systems, so that the energy sub-node system serving as the trusted node and the other energy sub-node systems add the data block to the end of their respective blockchains respectively.
According to another aspect of the present disclosure, there is provided a regional energy scheduling system, including: a plurality of energy sub-node systems as described above.
According to yet another aspect of the present disclosure, a computer-readable storage medium is provided, which stores computer instructions for execution by a processor to perform the method as described above.
According to the energy scheduling method, the energy sub-node system, the regional energy scheduling system and the storage medium, the scheduling strategy is generated by combining the existing energy states and the expected energy states of the energy sub-node system and other energy sub-node systems, so that the accuracy of the scheduling strategy can be improved; the optimal scheduling strategy is determined by adopting a negotiation arbitration mode, the optimal scheduling strategy is broadcasted to the regional energy scheduling system, and all energy sub-node systems jointly participate in the decision of the optimal scheduling strategy, so that the flexibility of regional energy scheduling can be improved, and the whole system can run more stably and reliably; by writing the scheduling processing information and/or the energy transaction information into the blockchain, the generation of the data blocks in the blockchain does not depend on a specific energy sub-node system or all nodes, the block chain data can be prevented from being tampered by malicious nodes, the safety of the block chain data is improved, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive exercise.
Fig. 1A is a schematic flow diagram of one embodiment of an energy scheduling method according to the present disclosure;
FIG. 1B is a schematic diagram of an embodiment of a regional energy scheduling system;
fig. 2A is a schematic flow chart illustrating a process of determining an optimal scheduling policy in an embodiment of the energy scheduling method according to the present disclosure;
FIG. 2B is a schematic diagram of one embodiment of determining a scheduling policy in a regional energy scheduling system;
FIG. 3 is a schematic flow chart diagram illustrating obtaining a desired energy state in one embodiment of an energy scheduling method according to the present disclosure;
FIG. 4A is a schematic flow chart diagram illustrating model training in one embodiment of an energy scheduling method according to the present disclosure;
FIG. 4B is a schematic flow chart illustrating prediction using a model in an embodiment of an energy scheduling method according to the present disclosure;
FIG. 5 is a schematic diagram of one embodiment of storing data in a regional energy scheduling system;
FIG. 6 is a block schematic diagram of one embodiment of an energy sub-node system according to the present disclosure;
fig. 7 is a block diagram of an energy status prediction module in an embodiment of an energy sub-node system according to the present disclosure.
Detailed Description
The present disclosure now will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the disclosure are shown. The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by one of ordinary skill in the art from the embodiments disclosed herein without making any creative effort, shall fall within the scope of protection of the present disclosure. The technical solution of the present disclosure is described in various aspects below with reference to various figures and embodiments.
The terms "first", "second", and the like are used hereinafter only for descriptive distinction and not for other specific meanings.
Fig. 1A is a schematic flowchart of an embodiment of an energy scheduling method according to the present disclosure, where the energy scheduling method is applied to a regional energy scheduling system, and the regional energy scheduling system includes: a plurality of energy sub-node systems. As shown in fig. 1A:
And 103, determining an optimal scheduling strategy corresponding to the regional energy scheduling system by all the energy sub-node systems based on a preset negotiation arbitration rule, and broadcasting the optimal scheduling strategy in the regional energy scheduling system.
And 104, the energy sub-node system performs corresponding scheduling and/or transaction processing based on the optimal scheduling strategy.
As shown in fig. 1B, the regional energy scheduling system includes a first energy sub-node system, a second energy sub-node system, … …, and an nth energy sub-node system. A first energy sub-node system, a second energy sub-node system, … …, and an Nth energy sub-node system corresponding to the first user, the second user, … …, and the Nth user, respectively.
Each energy sub-node system comprises electric equipment, an energy storage system, a photovoltaic power generation system, a control prediction system, a storage medium and the like. Each energy sub-node system is provided with a communication module, an energy block chain module, an electric energy transaction interface, a natural gas transaction interface, a water resource transaction interface, a heat transaction interface and the like. Each energy sub-node system sets the energy demand and the minimum reserve in each time period according to the condition of each user, and can set according to historical data. The whole regional energy dispatching system can be operated in a grid-connected mode with commercial power, heat supply, water supply and gas supply, and can also be operated in an off-grid mode. In the grid-connected operation stage or the off-grid operation stage, each energy sub-node system can sell redundant energy to other energy sub-node systems in the regional energy dispatching system through corresponding trading interfaces.
In the operation stage of the regional energy scheduling system, each energy sub-node system collects the existing energy state of the energy sub-node system and predicts to obtain the predicted energy state at the next moment. All energy sub-node systems can monitor and broadcast respective existing energy states and forecast energy states through communication, and energy state information sharing is carried out. And after the energy state information sharing is finished, each energy sub-node system respectively runs a scheduling algorithm to generate a scheduling strategy. And comparing the scheduling strategies of each energy sub-node system based on a preset negotiation arbitration rule, finally obtaining an optimal scheduling strategy, broadcasting the optimal scheduling strategy, and enabling all the energy sub-node systems to carry out corresponding scheduling and/or transaction processing based on the optimal scheduling strategy.
In one embodiment, determining the optimal scheduling policy based on preset negotiation arbitration rules may use a variety of methods. Fig. 2A is a schematic flowchart of determining an optimal scheduling policy in an embodiment of the energy scheduling method according to the present disclosure, as shown in fig. 2:
The energy sub-node system broadcasts its own scheduling strategy in the regional energy scheduling system by adopting various methods. For example, for at least one energy sub-node system in all the energy sub-node systems broadcasting its own scheduling policy in the regional energy scheduling system, the following steps are performed:
step one, all energy sub-node systems are in an activated state, and one energy sub-node system in the activated state broadcasts a scheduling strategy in a regional energy scheduling system.
And step two, judging whether the self scheduling strategy is superior to the received scheduling strategy by other energy sub-node systems in the activated state, and if not, setting the self in the deactivated state.
And step three, if the number of the energy sub-node systems in the activated state is greater than 1, determining that the next energy sub-node system in the activated state broadcasts the scheduling strategy of the next energy sub-node system in the regional energy scheduling system. And repeating the second step and the third step until only one energy sub-node system in the activated state exists.
If the energy sub-node system is in an activated state and has a token, the energy sub-node system broadcasts its own scheduling policy in the regional energy scheduling system and transmits the token to the next energy sub-node system in the activated state.
If only one energy sub-node system in the activated state exists, the energy sub-node system determines the scheduling strategy of the energy sub-node system as the optimal scheduling strategy, and broadcasts a scheduling strategy receiving message in the regional energy scheduling system to inform all the energy sub-node systems in the deactivated state of receiving the optimal scheduling strategy.
As shown in fig. 2B, each energy sub-node system broadcasts its existing energy state and expected energy state (future energy state) in the regional energy scheduling system through the communication module, and receives the existing energy state and expected energy state transmitted by other energy sub-node systems through the communication module. And each energy sub-node system adopts a preset scheduling algorithm to obtain the next scheduling trend of the energy scheduling system in the whole area, and generates a scheduling strategy. The scheduling algorithm may be any of a variety of existing scheduling algorithms.
And each energy sub-node system calculates the scheduling strategy of the energy scheduling system in the whole area, and compares the scheduling strategy with the scheduling strategies generated by other energy sub-node systems. In the comparison process, the comparison may be performed by means of token passing, for example, at the beginning, all energy sub-node systems are in an active state, the token is at the first energy sub-node system, and the first energy sub-node system broadcasts its own scheduling policy to other energy sub-node systems. And comparing the acquired scheduling strategy of the first energy sub-node system with the scheduling strategy of other energy sub-node systems, wherein the energy sub-node systems superior to the scheduling strategy of the first energy sub-node system are kept in an activated state, and the energy sub-node systems inferior to the scheduling strategy of the first energy sub-node system are set in an inactivated state.
There are various methods for determining the quality of the scheduling policy. For example, the goodness of the scheduling policy is determined by an objective function, which may be based on the goodness of the scheduling policy. The scheduling algorithm can be composed of an objective function and a solving framework, and the size of the result of the objective function represents the quality degree. For example, the objective function is the lowest energy consumption, and in all energy sub-node systems, the scheduling policy of the energy sub-node system with the smallest objective function is the optimal scheduling policy; the objective function is the highest user comfort level, and in all energy sub-node systems, the scheduling strategy of the energy sub-node system with the largest objective function is the optimal scheduling strategy.
And after the first energy sub-node system broadcasts the scheduling strategy of the first energy sub-node system, the token is sent to the second energy sub-node system. And if the second energy sub-node system is in the activated state, the self scheduling strategy is broadcasted to other energy node systems, and comparison is carried out, and if the second energy sub-node system is in the inactivated state, the token is transmitted to a third energy sub-node system. By analogy, the token will reach the last energy sub-node system, which will broadcast a scheduling policy receipt message to all other energy sub-node systems, instructing all inactive energy sub-node systems to prepare to accept the scheduling policy. The unique energy sub-node system in the activated state broadcasts the scheduling strategy (optimal scheduling strategy) of the unique energy sub-node system. And after receiving the scheduling strategy instruction, each energy sub-node system acts and can reset itself to be in an activated state.
In the arbitration process of the scheduling strategy, all the energy sub-node systems are in an activated state, a token (permission for sending the scheduling strategy to other energy sub-node systems) is arranged at the first energy sub-node system, and the first energy sub-node system broadcasts the scheduling strategy of the first energy sub-node system to the whole network. The token can only be transmitted between the energy sub-node systems in the active state, and when the token cannot be transmitted, the energy sub-node system held by the token is the last active node, that is, the scheduling policy of the energy sub-node system is the optimal scheduling policy.
The energy scheduling method in the embodiment can perform optimized scheduling on power generation, water, gas and heat in the regional energy scheduling system; each energy sub-node system shares the respective existing energy state and the expected energy state, the existing energy states and the expected energy states of other energy sub-node systems are comprehensively considered to generate the scheduling strategy, and the accuracy of the scheduling strategy can be improved.
Fig. 3 is a schematic flow chart of obtaining the expected energy state in an embodiment of the energy scheduling method according to the present disclosure, as shown in fig. 3:
And step 302, the energy sub-node system predicts the existing energy state by using the trained prediction model to obtain the expected energy state.
Training the predictive model may take a variety of approaches. Fig. 4A is a schematic flowchart of performing model training in an embodiment of the energy scheduling method according to the present disclosure, as shown in fig. 4A:
If the first stability detection is determined to pass, the energy sub-node system decomposes the historical energy detection signal to obtain a plurality of first sub-signals, step 402. The decomposition of the historical energy detection signal can adopt a variation modal decomposition method and the like.
In step 403, the energy sub-node system classifies the plurality of first sub-signals to obtain a first high-frequency sub-signal and a first low-frequency sub-signal.
In step 403, the energy sub-node system trains the first predictor model and the second predictor model respectively by using the first high-frequency sub-signal and the first low-frequency sub-signal. The first predictor model includes: markov chain models, etc.; the second predictor model includes: autoregressive moving average models, and the like.
In one embodiment, historical energy detection signals corresponding to electricity generation, water usage, gas usage, heat, etc. are obtained. For example, in training, an original energy detection signal corresponding to generation of electricity, water, gas, heat, or the like over a past period of time is obtained as a history energy detection signal.
The historical energy detection signal is an original signal, first stability detection is carried out on the original signal, if the original signal passes through the stability detection, the next step is carried out, and if the original signal does not pass through the stability detection, a difference operation needs to be carried out on the original signal. And carrying out first stability detection on the difference result, entering the next step after the condition is met, and continuing to carry out difference operation until the first stability detection is met if the condition is not met.
Since the result of prediction is poor for a signal with low stability, the stability of the signal can be improved by using differential operation, and the stability of prediction can be indirectly improved. Various differential operations may be employed, e.g.Assuming that a signal exists, the value of each sample point is aiI is the number of the sampling points, and the signal has N sampling points, i belongs to [1, N ]]Andd Z, Z representing a set of integers. The differential operation of the signal can be represented as:
bp＝ai-ai-1，i＝2,3,...N (1-1)；
wherein, bpN-1, which is the p-th value of the signal difference result.
And decomposing the signal processed in the last step by adopting a variation modal decomposition method to obtain a plurality of first sub-signals, classifying the first sub-signals according to frequency by adopting a preset frequency set value, and dividing the first sub-signals into first high-frequency sub-signals and first low-frequency sub-signals.
The first high frequency sub-signal is predicted using a markov chain model or the like. The Markov chain model can predict the random process at a next step by using a transition probability matrix mode. Training is performed by inputting the first high frequency sub-signal into a Markov chain model. After training, the trained Markov chain model is saved.
And predicting the first low-frequency sub-signal by using an autoregressive moving average model and the like. The autoregressive moving average model predicts the time sequence value of the next moment by adopting the time sequence values of a plurality of past moments. Training is performed by inputting the first low-frequency sub-signal into an autoregressive moving average model. After training, the trained autoregressive moving average model is saved.
The prediction of the expected energy state using the model may use a variety of methods. Fig. 4B is a schematic flowchart of prediction by using a model in an embodiment of the energy scheduling method according to the present disclosure, as shown in fig. 4B:
In step 503, the energy sub-node system classifies the plurality of second sub-signals to obtain a second high-frequency sub-signal and a second low-frequency sub-signal.
And step 504, the energy sub-node system respectively inputs the second high-frequency sub-signal and the second low-frequency sub-signal into the first predictor model and the second predictor model to obtain a first predictor result and a second predictor result.
The energy sub-node system generates 505 an expected energy state based on the first predictor result and the second predictor result.
In one embodiment, the energy detection signal may include detection signals of electricity generation, gas utilization, water, heat and the like, and the detection signals of electricity generation, gas utilization, water, heat and the like may be independently predicted, and an expected value at the next time may be obtained through prediction. In the prediction, an original energy detection signal corresponding to generation of electricity, water use, gas, heat, or the like in the past month, week, or the like is obtained as an existing energy detection signal. Performing second stability detection on the existing energy detection signal, and if the second stability detection is passed, performing the next step of processing; if the second stability detection is not passed, a differential operation needs to be performed on the existing energy detection signal. And performing stability detection on the difference result, entering the next step after the condition is met, and continuing to perform difference operation if the condition is not met until the second stability detection is met.
And decomposing the existing energy detection signal by adopting variational modal decomposition to obtain a plurality of second sub-signals. And classifying the second sub-signals according to the frequency by adopting a preset frequency set value, and dividing the second sub-signals into second high-frequency sub-signals and second low-frequency sub-signals. And obtaining a pre-stored Markov chain model and an autoregressive moving average model, predicting the variation trend of the second high-frequency sub-signal by adopting the Markov chain model, and predicting the variation trend of the second low-frequency sub-signal by adopting the autoregressive moving average model. And merging and superposing the predicted second high-frequency sub-signal variation trend and the predicted second low-frequency sub-signal variation trend to obtain a difference prediction result of a final prediction result, and performing reverse difference with the same difference times during data processing to obtain a final expected energy state.
For example, assume that the second high-frequency sub-signal trend is expressed as: gjJ is the serial number of the sampling point, and the variation trend of the second low-frequency sub-signal is ljJ is the sampling point number, the obtained signal Y is predictedj＝gj+ljWherein it is assumed that the values of M sample points are predicted, i.e., j ∈ [1, M]∩Z。
If a difference operation is performed while making a prediction, as in the above equation (1-1), bpN-1, which is the p-th value of the signal difference result. The inverse differential can be expressed as: the first value of the prediction is: a isN+1＝bN+Y1The remaining values are: a isN+j＝Yj-1+YjWhere j is ∈ [2, M ]]∩Z。
The first stability test and the second stability test may employ various test methods, for example, an existing unit root test method, i.e., an ADF test method. The Markov chain model can realize the prediction of a random process time sequence, and the autoregressive moving average model can realize the prediction of a time sequence with periodicity or slow change. The first high-frequency sub-signal and the second high-frequency sub-signal represent random components of the original energy detection signal; the first low-frequency sub-signal and the second low-frequency sub-signal represent components with periodicity and no significant variation trend in the original energy detection signal.
In the energy scheduling method in the above embodiment, an optimal scheduling policy is determined in a negotiation arbitration manner, and the optimal scheduling policy is broadcasted to the regional energy scheduling system, so that each energy child node performs a corresponding action; all energy sub-node systems participate in decision making together to determine an optimal scheduling strategy, compared with an energy scheduling system adopting centralized control, the flexibility of regional energy scheduling can be improved, and even if a plurality of energy sub-node systems are added in an already-operated system, the scheduling strategy does not need to be changed; the probability of easily falling into local optimum caused by the calculation of the scheduling strategy by a single node can be reduced, the generation speed of the system scheduling strategy is improved, and the data delay caused by data blockage in centralized control is improved;
in one embodiment, after the scheduling process and/or the energy transaction corresponding to the regional energy scheduling system is finished, each energy sub-node system writes the scheduling process information and/or the energy transaction information into each block chain. There may be various methods of writing the scheduling processing information and/or the energy transaction information into the respective block chains, for example, if the energy sub-node system is a trusted node and is granted a recording authority, a data block corresponding to the scheduling processing information and/or the energy transaction information is generated and broadcast to the other energy sub-node systems, so that the energy sub-node system and the other energy sub-node systems, which are trusted nodes, add the data block to the end of the respective block chains, respectively.
As shown in fig. 5, in the blockchain, there is a blockchain link point with Master flag, which is the generation node of the created block and has all control right to the whole blockchain. The block link point with the Master mark can be a node with a weak management function on the whole block chain, and the permission is given by the node.
The energy sub-node system (energy block chain module) can be used as a zone cross-chain node, and the first energy sub-node system, the second energy sub-node system and the third energy sub-node system are trusted nodes (trusted energy nodes) which are verified not to tamper with the block chain. When the block chain storage system runs, mine digging authorities (including recording authorities and the like) are authorized or deprived in the first energy sub-node system, the second energy sub-node system and the third energy sub-node system irregularly and randomly, but at least one recording authority has the mine digging authority at the same time.
And after the scheduling process and the energy transaction are finished, scheduling processing information and/or energy transaction information are/is issued to the block chain, the energy sub-node system with the mining authority generates a data block, the data block is broadcasted to other energy sub-node systems, and each energy sub-node system adds the data block to the tail of the block chain of the energy sub-node system.
Because the blockchain network has the same-chain consistency, the blockchain data of all the energy sub-node systems in the regional energy scheduling system are ensured to be consistent, and the integrity of the blockchain of the energy sub-node systems can also be ensured through an updating mechanism of the blockchain.
According to the energy scheduling method in the embodiment, the scheduling processing information and/or the energy transaction information are written into the block chain, all the energy sub-node systems are divided into the trusted nodes and the untrusted nodes by the block chain, and the data blocks in the block chain are generated without depending on a specific energy sub-node system or all the nodes by means of mutual endowment and deprivation of mining authority among the trusted nodes, so that tampering of data of the block chain by malicious nodes can be avoided, the safety of the block chain is improved, and the user experience is improved.
In one embodiment, as shown in fig. 6, the present disclosure provides an energy sub-node system 61, the energy sub-node system 61 being located in a regional energy scheduling system, including: an energy state prediction module 611, a scheduling policy processing module 612, a scheduling processing module 613, and an energy blockchain module 614. The energy sub-node system 61 may have a communication module through which data transmission and reception and the like are possible, and a transaction interface through which energy transaction and the like are possible.
The energy status prediction module 611 broadcasts the existing energy status and the expected energy status of the energy sub-node system to which it belongs in the regional energy scheduling system. The scheduling policy processing module 612 generates a scheduling policy based on the existing energy state and the expected energy state of the energy sub-node system to which it belongs, and the received existing energy state and expected energy state of the other energy sub-node systems.
The scheduling processing module 613 performs scheduling and/or transaction processing based on the optimal scheduling policy. All energy sub-node systems in the regional energy scheduling system determine an optimal scheduling strategy corresponding to the regional energy scheduling system based on a preset negotiation arbitration rule, and broadcast the optimal scheduling strategy in the regional energy scheduling system.
At least one energy sub-node system in all the energy sub-node systems broadcasts the scheduling strategy of the energy sub-node system in the regional energy scheduling system. If the scheduling policy processing module 612 determines that the scheduling policy of the energy sub-node system to which the scheduling policy belongs is better than the received scheduling policies of all other energy sub-node systems, the scheduling policy of the energy sub-node system to which the scheduling policy belongs is determined as the optimal scheduling policy and is broadcasted in the regional energy scheduling system.
If the scheduling policy processing module 612 determines that the energy sub-node system to which the scheduling policy processing module belongs is in the activated state and has the token, the scheduling policy processing module broadcasts the scheduling policy of the energy sub-node system to which the scheduling policy processing module belongs in the regional energy scheduling system, and transmits the token to the next energy sub-node system in the activated state.
If the scheduling policy processing module 612 determines that only the energy sub-node system to which the scheduling policy processing module belongs is in the activated state, the scheduling policy of the energy sub-node system to which the scheduling policy processing module belongs is determined as the optimal scheduling policy, and a scheduling policy receiving message is broadcasted in the regional energy scheduling system to notify all the energy sub-node systems in the deactivated state of receiving the optimal scheduling policy.
After the scheduling processing and/or energy transaction corresponding to the regional energy scheduling system is finished, the energy blockchain module 614 writes the scheduling processing information and/or the energy transaction information into the blockchain of the energy sub-node system to which the energy blockchain module belongs. If the blockchain module 614 determines that the energy sub-node system to which the blockchain module belongs is a trusted node and is granted a recording authority, a data block corresponding to the scheduling processing information and/or the energy transaction information is generated and broadcast to other energy sub-node systems, so that the energy sub-node system and the other energy sub-node systems which are trusted nodes respectively add the data block to the tail of the respective blockchain.
In one embodiment, as shown in fig. 7, the energy status prediction module 611 includes: a model training unit 6111 and a state prediction unit 6112. The model training unit 6111 builds a prediction model and trains the prediction model. The state prediction unit 6112 predicts the existing energy state by using the trained prediction model to obtain the expected energy state.
The model training unit 6111 obtains the historical energy detection signal, and performs a first stability detection on the historical energy detection signal. If it is determined that the first stability detection is passed, the model training unit 6111 decomposes the historical energy detection signal to obtain a plurality of first sub-signals. The model training unit 6111 classifies the plurality of first sub-signals to obtain a first high-frequency sub-signal and a first low-frequency sub-signal, and trains the first predictor model and the second predictor model by using the first high-frequency sub-signal and the first low-frequency sub-signal respectively.
The state prediction unit obtains an existing energy detection signal and performs a second stability detection on the existing energy detection signal. If it is determined that the second stability detection is passed, the state prediction unit 6112 decomposes the existing energy detection signal to obtain a plurality of second sub-signals. The state prediction unit 6112 classifies the plurality of second sub-signals to obtain a second high-frequency sub-signal and a second low-frequency sub-signal, and inputs the second high-frequency sub-signal and the second low-frequency sub-signal to the first predictor model and the second predictor model, respectively, to obtain a first predictor result and a second predictor result. The state prediction unit 6112 generates the expected energy state based on the first predictor result and the second predictor result.
In one embodiment, the present disclosure provides a regional energy scheduling system comprising a plurality of energy sub-node systems as in any of the above embodiments.
The energy scheduling method, the energy sub-node system, the regional energy scheduling system and the storage medium in the above embodiments can perform optimized scheduling for power generation, water, gas, heat and the like in the regional energy scheduling system; the accuracy of the scheduling strategy can be improved by comprehensively considering the existing energy states and the expected energy states of the self energy sub-node system and other energy sub-node systems to generate the scheduling strategy; determining an optimal scheduling strategy by adopting a negotiation arbitration mode, and broadcasting the optimal scheduling strategy to a regional energy scheduling system to enable each energy child node to perform corresponding action; all energy sub-node systems participate in decision making together to determine an optimal scheduling strategy, so that the flexibility of regional energy scheduling can be improved, and when one link fails, the system cannot be crashed; scheduling processing information and/or energy transaction information are written into the block chain, the generation of data blocks in the block chain does not depend on a specific energy sub-node system or all nodes, malicious nodes can be prevented from tampering block chain data, the safety of the block chain data is improved, and the user experience is improved.
The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
The description of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (19)
1. An energy scheduling method is applied to a regional energy scheduling system, and the regional energy scheduling system comprises: a plurality of energy sub-node systems; the method comprises the following steps:
the energy sub-node system broadcasts the existing energy state and the expected energy state of the energy sub-node system in the regional energy dispatching system;
the energy sub-node system generates a scheduling strategy based on the current energy state and the expected energy state of the energy sub-node system and the received current energy state and expected energy state of other energy sub-node systems;
all the energy sub-node systems determine an optimal scheduling strategy corresponding to the regional energy scheduling system based on a preset negotiation arbitration rule, and broadcast the optimal scheduling strategy in the regional energy scheduling system;
if the energy sub-node system determines that the scheduling strategy of the energy sub-node system is superior to the received scheduling strategies of all other energy sub-node systems, the scheduling strategy of the energy sub-node system is determined to be the optimal scheduling strategy and is broadcasted in the regional energy scheduling system;
the energy sub-node system carries out corresponding scheduling and/or transaction processing based on the optimal scheduling strategy;
wherein the broadcasting of the scheduling policy of at least one energy sub-node system of all the energy sub-node systems in the regional energy scheduling system comprises:
step one, all the energy sub-node systems are in an activated state, and one energy sub-node system in the activated state broadcasts a scheduling strategy in the regional energy scheduling system;
step two, judging whether the self scheduling strategy is superior to the received scheduling strategy by other energy sub-node systems in the activated state, and if not, setting the self in the deactivated state;
step three, if the number of the energy sub-node systems in the activated state is more than 1, determining that the next energy sub-node system in the activated state broadcasts a scheduling strategy in the regional energy scheduling system;
and repeating the second step and the third step until only one energy sub-node system in the activated state exists.
2. The method of claim 1, further comprising:
and if the energy sub-node system is in an activated state and has a token, the energy sub-node system broadcasts a scheduling strategy of the energy sub-node system in the regional energy scheduling system and transmits the token to the next energy sub-node system in the activated state.
3. The method of claim 1, further comprising:
if only one energy sub-node system in the activated state exists, the energy sub-node system determines the scheduling strategy of the energy sub-node system as the optimal scheduling strategy, and broadcasts a scheduling strategy receiving message in the regional energy scheduling system to inform all the energy sub-node systems in the deactivated state of receiving the optimal scheduling strategy.
4. The method of claim 1, further comprising:
the energy sub-node system establishes a prediction model and trains the prediction model;
and the energy sub-node system predicts the existing energy state by using the trained prediction model to obtain the expected energy state.
5. The method of claim 4, the training the predictive model comprising:
the energy sub-node system obtains a historical energy detection signal and carries out first stability detection on the historical energy detection signal;
if the first stability detection is determined to pass, the energy sub-node system decomposes the historical energy detection signal to obtain a plurality of first sub-signals;
the energy sub-node system classifies the plurality of first sub-signals to obtain a first high-frequency sub-signal and a first low-frequency sub-signal;
and the energy sub-node system respectively trains a first predictor model and a second predictor model by using the first high-frequency sub-signal and the first low-frequency sub-signal.
6. The method of claim 5, the energy sub-node system predicting the existing energy state using the trained predictive model, the obtaining the expected energy state comprising:
the energy sub-node system obtains an existing energy detection signal and carries out second stability detection on the existing energy detection signal;
if the second stability detection is determined to pass, the energy sub-node system decomposes the existing energy detection signal to obtain a plurality of second sub-signals;
the energy sub-node system classifies the plurality of second sub-signals to obtain a second high-frequency sub-signal and a second low-frequency sub-signal;
the energy sub-node system respectively inputs the second high-frequency sub-signal and the second low-frequency sub-signal into the first predictor model and the second predictor model to obtain a first predictor result and a second predictor result;
the energy sub-node system generates the expected energy state based on the first predictor result and the second predictor result.
7. The method of claim 5, further comprising:
the first predictor model includes: a Markov chain model; the second predictor model includes: autoregressive moving average model.
8. The method of claim 1, further comprising:
and after the scheduling processing and/or the energy transaction corresponding to the regional energy scheduling system are finished, each energy sub-node system writes scheduling processing information and/or energy transaction information into each block chain.
9. The method of claim 8, wherein writing scheduling information and/or energy transaction information into the respective blockchains by each energy sub-node system comprises:
and if the energy sub-node system is a credible node and is granted with the recording authority, generating a data block corresponding to scheduling processing information and/or energy transaction information, and broadcasting the data block to other energy sub-node systems, so that the energy sub-node system as a credible node and the other energy sub-node systems respectively add the data block to the tail of each block chain.
10. The method of any one of claims 1 to 9,
the energy sources corresponding to the existing energy source state and the expected energy source state include: at least one of electric energy, heat energy, water and gas.
11. An energy sub-node system in a regional energy scheduling system, comprising:
the energy state prediction module is used for broadcasting the existing energy state and the expected energy state of the energy sub-node system to which the energy sub-node system belongs in the regional energy scheduling system;
the scheduling strategy processing module is used for generating a scheduling strategy based on the existing energy state and the expected energy state of the energy sub-node system to which the scheduling strategy belongs and the received existing energy state and the expected energy state of other energy sub-node systems;
the scheduling processing module is used for carrying out corresponding scheduling and/or transaction processing based on the optimal scheduling strategy;
all energy sub-node systems in the regional energy scheduling system determine an optimal scheduling strategy corresponding to the regional energy scheduling system based on a preset negotiation arbitration rule, and broadcast the optimal scheduling strategy in the regional energy scheduling system;
at least one energy sub-node system in all the energy sub-node systems broadcasts a scheduling strategy of the energy sub-node system in the regional energy scheduling system;
the scheduling strategy processing module is used for determining the scheduling strategy of the energy sub-node system to which the energy sub-node system belongs as the optimal scheduling strategy and broadcasting the optimal scheduling strategy in the regional energy scheduling system if the scheduling strategy of the energy sub-node system to which the energy sub-node system belongs is determined to be superior to the received scheduling strategies of all other energy sub-node systems;
the scheduling strategy processing module is further configured to broadcast the scheduling strategy of the energy sub-node system to which the energy sub-node system belongs in the regional energy scheduling system and transmit the token to the next energy sub-node system in the activated state if the energy sub-node system to which the energy sub-node system belongs is determined to be in the activated state and has the token;
and the energy sub-node system in the activated state judges whether the scheduling strategy of the energy sub-node system is superior to the received scheduling strategy, and if not, the energy sub-node system is set to be in the deactivated state.
12. The energy sub-node system of claim 11,
the scheduling policy processing module is further configured to determine, if it is determined that only the energy sub-node system to which the scheduling policy processing module belongs is in an activated state, the scheduling policy of the energy sub-node system to which the scheduling policy processing module belongs as the optimal scheduling policy, and broadcast a scheduling policy receiving message in the regional energy scheduling system, so as to notify all the energy sub-node systems in an inactivated state of receiving the optimal scheduling policy.
13. The energy sub-node system of claim 11,
the energy state prediction module comprises:
the model training unit is used for establishing a prediction model and training the prediction model;
and the state prediction unit is used for predicting the existing energy state by using the trained prediction model to obtain the expected energy state.
14. The energy sub-node system of claim 13,
the model training unit is further used for obtaining a historical energy detection signal and carrying out first stability detection on the historical energy detection signal; if the first stability detection is determined to pass, decomposing the historical energy detection signal to obtain a plurality of first sub-signals; classifying the plurality of first sub-signals to obtain a first high-frequency sub-signal and a first low-frequency sub-signal; and respectively training a first predictor model and a second predictor model by using the first high-frequency sub-signal and the first low-frequency sub-signal.
15. The energy sub-node system of claim 14,
the state prediction unit is further configured to obtain an existing energy detection signal, and perform second stability detection on the existing energy detection signal; if the second stability detection is determined to pass, decomposing the existing energy detection signal to obtain a plurality of second sub-signals; classifying the plurality of second sub-signals to obtain a second high-frequency sub-signal and a second low-frequency sub-signal; inputting the second high-frequency sub-signal and the second low-frequency sub-signal into the first predictor model and the second predictor model respectively to obtain a first predictor result and a second predictor result; generating the expected energy state based on the first predictor result and the second predictor result.
16. The energy sub-node system of claim 11, further comprising:
and the energy block chain module is used for writing scheduling processing information and/or energy transaction information into a block chain of an energy sub-node system to which the energy block chain module belongs after scheduling processing and/or energy transaction corresponding to the regional energy scheduling system is finished.
17. The energy sub-node system of claim 16,
and the block chain module is further used for generating a data block corresponding to scheduling processing information and/or energy transaction information if the energy sub-node system to which the block chain module belongs is determined to be a trusted node and is granted with a recording authority, and broadcasting the data block to other energy sub-node systems, so that the energy sub-node system serving as the trusted node and the other energy sub-node systems respectively add the data block to the tail of the respective block chain.
18. A regional energy scheduling system comprising:
a plurality of energy sub-node systems according to any one of claims 11 to 17.
19. A computer-readable storage medium having stored thereon computer instructions for execution by a processor to perform the method of any one of claims 1 to 10.
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