Detailed Description
In order to make the technical solution of the present application better understood by those skilled in the art, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application. All other embodiments, based on the embodiments of the application, which would be apparent to one of ordinary skill in the art without undue burden are intended to be within the scope of the application.
It should be noted that, the information and data related to the user in the embodiments of the present disclosure are information and data authorized by the user or fully authorized by the related parties, and the processes of collecting, storing, using, processing, transmitting, providing, disclosing and applying the related data all comply with relevant laws and regulations and standards, take necessary security measures, do not violate the public welcome, and provide corresponding operation entries for the user or the related parties to select authorization or rejection.
It should also be noted that in the embodiments of the present disclosure, some existing solutions in the industry such as software, components, models, etc. may be mentioned, and they should be considered as exemplary, only for illustrating the feasibility of implementing the technical solution of the present disclosure, but not meant to imply that the applicant has or must not use the solution.
As previously mentioned, market trading for new energy sources relies primarily on traditional power market mechanisms including utilization of market price signals, matching of electricity generation and demand, etc. In order to cope with the volatility of new energy, some market designs have introduced energy storage systems as a means of balancing power supply and demand. The common implementation method in the market comprises the following steps:
price mechanism-the electricity market directs electricity production and consumption through market price. Market prices are adjusted in real time according to supply and demand relationships, thereby encouraging or suppressing the production and consumption of electricity.
The energy storage system is used for storing electric power which cannot be consumed immediately in a high-demand or high-yield period and releasing reserve electric power when the demand is increased or the supply is reduced. Energy storage technologies include Battery Energy Storage Systems (BESS), pumped storage, and the like.
And the demand response is to guide the consumers to reduce electricity consumption in the peak period of the power demand through price signals or incentive measures so as to reach the balance of supply and demand.
And predicting and scheduling, namely predicting the generated energy of wind energy and solar energy by using weather forecast and historical data, and performing power scheduling based on a prediction result.
The method can cope with the challenges of new energy sources through market mechanisms and technical means, but the following problems still exist in practical application:
Price fluctuations and market instability-traditional power market price mechanisms can experience dramatic price fluctuations in the face of new energy fluctuations, which negatively impact market participants and system stability. For example, uncertainty in the amount of solar and wind energy production can lead to dramatic fluctuations in electricity prices.
The energy storage system has the limitation that although the energy storage system can balance power supply and demand, the problems of high cost, limited charge and discharge efficiency, short service life and the like limit the large-scale application of the energy storage system. In addition, the energy storage system is complex to dispatch and manage, and strategies need to be adjusted in real time to adapt to market demands.
Implementation difficulty of demand response-efficient implementation of demand response schemes requires consumers to be sensitive to electricity prices and have the ability to regulate electricity usage. In practice, the consumer's response may not be in time or in place, thereby affecting the supply-demand balancing effect.
The prediction accuracy is insufficient, although the existing power generation prediction method can provide approximate prediction data, when the wind speed and the illumination intensity change severely, the prediction error is still larger, and the scheduling efficiency of the power system is affected.
The traditional optimization model only pays attention to a single target, such as economic benefit maximization or resource utilization efficiency, and other key factors such as market stability are ignored, so that multiple demands cannot be met in practical application.
In summary, although the prior art provides a solution for new energy trading in the electric market, there are significant shortcomings in coping with new energy fluctuation, system stability and comprehensive optimization.
In order to solve the problems, the embodiment of the specification provides an electric power market transaction strategy optimization method, which comprises the steps of obtaining historical transaction data of an electric power market, determining transaction parameters of the electric power market based on the historical transaction data, wherein the historical transaction data comprise transaction data among a power generation end, a power distribution network and a power utilization end, constructing an electric power market transaction optimization model based on the determined transaction parameters, obtaining real-time transaction data and real-time equipment state data of the electric power market, processing the real-time transaction data and the real-time equipment state data by using the electric power market transaction optimization model, and optimizing the transaction strategy of the electric power market based on a processing result. According to the method, multiple aspects can be comprehensively considered, a more comprehensive and reliable electric power market transaction optimization model is constructed, and through real-time processing and feedback of market data, the strain capacity of the electric power market is improved, the stability of the electric power market is improved, and the control of the electric power market is more accurate and reliable.
It can be understood that the method provided by the application can be applied to electronic equipment, and the electronic equipment can refer to electronic equipment with data calculation, processing and storage capabilities. The electronic device may be a terminal such as a PC (personal computer), a tablet, a smart phone, a wearable device, a smart robot, or a server. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing service.
The method for optimizing the electric power market trading strategy provided by the embodiment of the application is described below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an optimization method for an electric power market trading strategy according to an embodiment of the present disclosure. Although the description provides methods and apparatus structures as shown in the examples or figures described below, more or fewer steps or modular units may be included in the methods or apparatus, whether conventionally or without inventive effort. In the steps or the structures where there is no necessary causal relationship logically, the execution order of the steps or the module structure of the apparatus is not limited to the execution order or the module structure shown in the embodiments or the drawings of the present specification. The described methods or module structures may be implemented in a device, server or end product in practice, in a sequential or parallel fashion (e.g., parallel processor or multi-threaded processing environments, or even distributed processing, server cluster implementations) as shown in the embodiments or figures. As shown in fig. 1, the method may include:
S101, acquiring historical transaction data of an electric power market, and determining transaction parameters of the electric power market based on the historical transaction data, wherein the historical transaction data comprises transaction data among an electric power generation end, an electric distribution network and an electric power utilization end.
It may be appreciated that the historical transaction data may include new energy used by the power generation device at the power generation end when generating power, corresponding power generation amount, input amount of the energy storage device, output amount of the energy storage device, power received by the power distribution network, power transported by the power distribution network to the power utilization end, power received by the power utilization end, and power price in the power market. In other embodiments, other transaction data may be included in the historical transaction data, as the embodiments of the present disclosure are not limited in this regard. In addition, the historical transaction data can be acquired, and meanwhile, the device states of various devices in the electric power market can be acquired, wherein the devices can be power generation devices, energy storage devices and the like.
In some embodiments of the present description, determining the transaction parameters of the power market based on the historical transaction data may include:
clustering the historical transaction data to obtain a plurality of data subsets;
and determining the association relation among the plurality of data subsets, and determining the transaction parameters corresponding to the data subsets based on the data in the data subsets and the association relation among the plurality of data subsets.
It can be understood that after the historical transaction data is obtained, the historical transaction data can be clustered to obtain a plurality of data subsets, each data subset can correspond to one transaction parameter, and further the transaction parameters corresponding to each data subset can be determined through analysis of the data in the data subsets and the association relationship of the data among the data subsets. Furthermore, the association relationship obtained by analysis can also be used for constructing an electric power market transaction optimization model subsequently. In the embodiment of the specification, through the analysis of the association relationship between the clusters and the data, compared with the data source directly based on the acquired historical transaction data, the method has the advantages that the transaction parameters are determined and the power market transaction optimization model is based, more accurate and reliable results can be obtained, and the constructed model can be more quickly adapted to the change of the current power market transaction environment, so that the power market transaction optimization model more suitable for the current power market is obtained.
In some embodiments of the present specification, the clustering process for the historical transaction data may include a K-means clustering algorithm, a hierarchical clustering algorithm, a neighbor propagation clustering algorithm, and the like, and specifically may be selected based on the data features of the historical transaction data, which is not limited in this specification.
In some embodiments of the present disclosure, determining the association relationship between the plurality of data subsets may be implemented by using methods such as Apriori algorithm, FP-growth algorithm, etc., or may obtain the association relationship between the data subsets by simulating the mapping relationship between the data of different data subsets, or may further analyze the data of the plurality of data subsets by using an artificial intelligent network model to determine the association relationship between the data subsets, so as to obtain the transaction parameters corresponding to the data subsets.
In some embodiments of the present disclosure, before clustering the historical transaction data, preprocessing such as data cleaning, outlier detection processing, and data format conversion may be performed on the historical transaction data, so as to provide a basis for accuracy of subsequent data clustering and association analysis between data.
S102, constructing an electric power market transaction optimization model based on the determined transaction parameters.
It can be appreciated that when the electric power market transaction model is constructed based on the transaction parameters, the model construction can also be performed in combination with the association relationship between the transaction parameters determined in the foregoing steps. By way of example, the objective function and the corresponding key parameters in the model can be predefined, then the mathematical model integrating the mathematical model and the actual transaction scene is constructed based on the association relation between each transaction parameter and the key parameter and the mathematical model of the key parameters, so that the constructed model can rapidly and accurately reflect the change of the electric power market transaction scene. Furthermore, when the power market transaction optimization model is constructed, the power market transaction optimization model can be constructed by combining historical operation data of all the terminal devices in the power market.
In some embodiments of the present description, the power market transaction optimization model may include a power market stabilization function, a resource utilization function, an economic benefit function, a market constraint.
Illustratively, the electric power market stabilization function, the resource utilization function, and the economic benefit function may be respectively constructed for the purposes of maximizing electric power market stability, maximizing resource utilization, and maximizing economic benefit, and the market constraint condition may be constructed for electric power supply and demand balance, operation constraints of various devices (including, for example, a generator set, an energy storage device, and the like) in the market, an electric power demand curve, and the like.
As described with reference to fig. 2, in some embodiments of the present disclosure, the step S102 of constructing the power market transaction optimization model based on the determined transaction parameters may specifically include:
s1021, determining the transaction scene type corresponding to each transaction parameter, and classifying the transaction parameters based on the transaction scene type of each transaction parameter.
It can be understood that, in order to quickly construct and obtain each objective function, transaction scene types corresponding to and associated with each objective function can be predefined, and then when the power market optimization model is constructed based on the transaction parameters, the transaction scene types corresponding to the transaction parameters can be determined first so as to classify the transaction parameters, and then the construction of the subsequent objective functions is performed based on classification results.
And S1022, constructing an objective function corresponding to each transaction scene type based on the classification result, wherein the transaction scene type comprises a market stabilization scene, a resource utilization scene and an economic benefit scene.
It will be appreciated that the transaction scenario type is an example of an embodiment of the present disclosure, and in other embodiments the transaction scenario type may also include more types, such as an environment friendly scenario, a social benefit scenario, etc., which is not limited in this disclosure.
S1023, determining a trading object of the electric power market based on the historical trading data.
It can be appreciated that the transaction objects may include transaction objects at each of a power generation end, a power distribution network and a power utilization end in the power market, for example, a power generation set, an energy storage device, power equipment in the power distribution network, a user of the power utilization end, and the like, and further, the constraint condition may be constructed based on the transaction objects.
S1024, determining the operation constraint condition of each transaction object and the transaction constraint condition among the transaction objects.
It will be appreciated that the operational constraints are used to characterize operational constraints corresponding to a single transaction, the transaction constraints are used to characterize transaction constraints between at least two transaction objects, and the operational constraints as well as the transaction constraints may be used as market constraints. By way of example, the operating constraints may be, for example, operating constraints of a generator set, operating constraints of an energy storage device, etc., and the transaction constraints may be, for example, power supply and demand constraints, etc. The operation constraint of the generator set may be defined based on an operation parameter of the generator set, for example, may be defined based on a related parameter such as a power generation amount of the generator set, a power generation rate of the generator set, etc., and the operation constraint of the energy storage device may be defined based on an operation parameter of the energy storage device, for example, may be defined based on a charge rate, a discharge rate, a charge amount, etc. of the energy storage device. Of course, the operating constraints and transaction constraints may also include constraints related to other transaction objects, which are not limited in this specification.
S1025, constructing the power market transaction optimization model based on the objective function, the operation constraint condition and the transaction constraint condition corresponding to each transaction scene.
Illustratively, after obtaining the plurality of objective functions, the operation constraint condition and the transaction constraint condition, the power market transaction optimization model may be constructed by a linear programming or a split-line programming mode, which is not limited in this specification.
In some embodiments of the present description, the power market transaction optimization model may be represented by the following formula:
Eeconomic＝∑(Pmarket×Qpower-Cgeneration-Cstorage);
Estability＝-Var(Pmarket)+Balance(Ddemand-Qsupply);
∑Qsupply＝∑Ddemand;
0≤Estorage(t)≤Emax;
Pmin≤Pgeneration≤Pmax;
Wherein Eeconomic represents economic benefits of the electric power market, Pmarket represents electric power price of the electric power market, Qpower represents electric power supply quantity in the electric power market, Cgeneration represents power generation cost of the electric power market, Cstorage represents energy storage cost of the electric power market, Eefficiency represents resource utilization efficiency of the electric power market, Ravailable represents available resource quantity of the electric power market, Estability represents stability of the electric power market, var (Pmarket) represents variance of electric power price of the electric power market, Ddemand and Qsupply respectively represent electric power demand quantity and electric power supply quantity of the electric power market, Estorage (t) represents energy storage electric quantity of the electric power market at time t, Emax represents maximum energy output quantity of energy storage equipment of the electric power market, Pgeneration represents power generation quantity of power generation end of the electric power market, and Pmin and Pmax respectively represent minimum power generation quantity and maximum power generation quantity of power generation equipment of the power generation end.
In some embodiments of the present disclosure, an economic benefit function may be constructed based on an electric market price and cost data, where the cost data may include an operation cost of each end transaction object, a resource utilization efficiency function may include an effective usage degree of a power generation end resource and an energy storage resource, and may be obtained by implementing a resource monitoring system to monitor data, and a market stability function may include a market price fluctuation degree, a supply and demand balance condition, and the like, to ensure accurate calculation of market price fluctuation, and to consider an effect of a real-time difference between demand and supply on market stability, a market analysis tool and a data collection platform are required to provide a data base.
In some embodiments of the present description, after determining constraints including operational constraints, trade constraints, etc., the constraints may be integrated into a multi-objective function to ensure that the optimization results are within practical limits, and the constraints may be processed using an optimization algorithm that will ensure that all constraints are satisfied by solving linear or nonlinear programming problems. In this process, the accuracy of the constraints and the effectiveness of the optimization algorithm can be ensured by using data acquisition and processing tools (e.g., real data monitoring systems), mathematical modeling software (e.g., the SciPy library in MATLAB or Python), and optimization solution tools (e.g., gurobi or CPLEX), etc.
And S103, acquiring real-time transaction data and real-time equipment state data of the electric power market.
It is understood that the real-time transaction data may include real-time monitoring data related to transactions between the power generation side, the power distribution network, and the power usage side. For example, the power generation device at the power generation end uses the real-time new energy and the corresponding real-time power generation amount, the real-time input amount of the energy storage device, the real-time output amount of the energy storage device, the power quantity received by the power distribution network in real time, the real-time power quantity transported to the power utilization end by the power distribution network, the real-time power quantity received by the power utilization end, the real-time power price in the power market, and the like.
And S104, processing the real-time transaction data and the real-time equipment state data by using the electric power market transaction optimization model, and optimizing the transaction strategy of the electric power market based on a processing result.
In some embodiments of the present description, processing the real-time transaction data and real-time device status data using the power market transaction optimization model may include:
preprocessing the real-time transaction data and the real-time equipment state data, wherein the preprocessing comprises data cleaning, outlier detection processing and data format conversion;
Inputting the preprocessed electric power market transaction optimization model, and solving the electric power market transaction optimization model by utilizing an optimization algorithm to obtain a target transaction strategy.
In some embodiments of the present disclosure, the optimization algorithm may include a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm, or a multi-objective near-algorithm, through which a plurality of objective functions may be optimized on the premise of meeting constraints, then the objective functions and constraints are input into an optimization solving tool, such as SciPy library or Gurobi solver in OptimizationToolbox, python of MATLAB, through which complex mathematical models and solving processes may be processed, and finally, the validity of the models and the solving results are verified, and whether the optimizing results meet all constraints may be checked, and the optimization scheme may be adjusted and optimized based on the checking results. The accuracy of the model and the effectiveness of the solution can be ensured through the use of data processing tools, mathematical modeling software and optimization solution tools.
Further, when the optimization algorithm is selected, a proper algorithm can be selected according to a specific problem, and then setting of algorithm parameters and implementation of the optimization process are performed. The following describes an optimization process of the optimization algorithm by taking a genetic algorithm, a particle swarm algorithm and a simulated annealing algorithm as examples.
In some embodiments of the present description, a genetic algorithm may be used to optimize an optimization model of an electric market transaction, and the optimization step may include initializing a population, generating an initial population of a set of solutions in a random manner, defining fitness functions, evaluating the goodness of each solution, for example using objective function values, generating a new generation population by applying selection, crossover and mutation operations, wherein the selection operations are applied to select individuals with high fitness, the crossover operations are applied to generate new individuals, the mutation operations are applied to increase the diversity of the population, iteratively performing the operations until termination conditions are met, such as maximum algebra or convergence requirements, and finally selecting an optimal solution from the population.
In some embodiments of the present description, a particle swarm algorithm may be used to optimize the power market transaction optimization model, and the optimizing step may include initializing a particle swarm, each particle representing a solution, defining an objective function as an fitness evaluation criterion, updating a speed and a position of the particle, performing a speed update based on a current particle optimal solution and a global optimal solution, iteratively performing the position and the speed update until a termination condition is satisfied, and selecting an optimal solution from the particle swarm.
In some embodiments of the present description, a simulated annealing algorithm may be employed to optimize the power market transaction optimization model, and the optimization step may include initializing a current solution and an initial temperature, defining an objective function as an evaluation criterion, generating a neighborhood solution and calculating its objective function value, deciding whether to accept the neighborhood solution based on an acceptance criterion, such as using a Metropolis criterion, gradually reducing the temperature to reduce the probability of accepting a bad solution, iterating until the temperature falls to a predetermined value, and finally obtaining an optimal solution as a result.
In some embodiments of the present description, during the optimization process using any of the above-described optimization algorithms, appropriate optimization tools and programming environments, such as SciPy library of OptimizationToolbox, python of MATLAB or specialized optimization solvers, may be used to handle complex computational and optimization problems and to provide the implementation and debugging functions of the algorithms, ensuring the implementation of the algorithms and the efficiency of the optimization process through efficient cooperation of data processing, mathematical modeling and optimization tools.
In some embodiments of the present description, the transaction policy of the electric power market may include an electric power supply policy, an energy storage policy, and an operating parameter of the power generating end device.
It can be appreciated that after the real-time transaction data and the real-time equipment state data are processed by using the power market transaction optimization model, at least one of the power supply strategy, the energy storage strategy and the working parameters of the power generation end equipment can be adjusted based on the processing result so as to adapt to the current transaction scenario of the power market.
In some embodiments of the present disclosure, a data collection processing system may be further established for obtaining transaction data of the electric power market in real time and performing feedback adjustment, where the collected data of the system may include information such as real-time electric power market price, power generation and energy storage data, demand prediction, and the like. The data processing process specifically comprises the steps of establishing a data acquisition processing system, acquiring real-time data from market data providers by using an API (application program interface), wherein the real-time data can be used for predicting data such as electric market price and demand, generating capacity, energy storage state and the like, preprocessing the collected data, including data cleaning, abnormal value detection and data format conversion, so as to ensure data quality, updating a database in real time, storing the processed data in the database, such as a relational database MySQL or a NoSQL database MongoDB, transmitting the real-time data to an optimization model according to the demand of an optimization algorithm, updating the model input through the data interface, ensuring that optimization calculation is based on the latest market information, implementing a feedback mechanism, adjusting the market strategy according to the optimization result, adjusting the electric power supply and energy storage strategy in real time, transmitting adjustment instructions to an actual operating system such as a power generation control system or an energy storage management system through a control system, monitoring the performance of the data acquisition processing system, ensuring the timeliness and accuracy of data processing and feedback adjustment, and carrying out system maintenance and upgrading to adapt to market change. When the system is configured, the data acquisition processing system can be provided with stable network connection and API interface, and the system is associated with data analysis software (such as Pandas library of Python or MATLAB) and database management system (such as MySQL or MongoDB) to process real-time data based on the cooperative work of a plurality of systems or tools, and further, after the optimized transaction strategy is determined, feedback adjustment on the electric market can depend on the interface of the control system and actual electric equipment, so as to ensure that the optimization result can be effectively applied to market operation.
In the embodiment of the specification, the following beneficial effects can be achieved through a multi-objective optimization new energy distribution and storage power market trading algorithm:
firstly, the method can pay attention to maximization of economic benefit, maximization of resource utilization efficiency and maximization of market stability simultaneously, overcomes the limitation of a traditional single-objective optimization model, and can balance economic benefit, resource use and market stability while optimizing an electric power market trading strategy by comprehensively considering a plurality of objectives, thereby realizing a globally optimal solution.
Secondly, by introducing an objective function with maximized market stability, the severe fluctuation of the price of the electric power market can be effectively reduced, the overall stability of the market is improved, the uncertainty caused by the fluctuation of the price is reduced, the interests of market participants are protected, and the reliability and the attractiveness of the market are improved.
Thirdly, by setting an objective function with maximized resource utilization efficiency, new energy and energy storage resources can be efficiently configured and used, resource waste is avoided, resource scheduling and utilization can be optimized, overall operation efficiency of the system is improved, and effective utilization of the new energy resources is enhanced.
Fourthly, market data can be obtained in real time through the introduction of the data processing module, and feedback adjustment is carried out according to the latest information, so that dynamic optimization strategies can be changed according to actual market conditions and demands, the response speed and the adaptability of the system are improved, and the optimization scheme is ensured to be always based on the latest market information.
Fifthly, through optimization of the economic benefit maximization objective function, the overall economic benefit of the electric power market can be effectively improved, and the cost expenditure can be reduced and the benefit can be increased through a scientific transaction strategy, so that the economic benefit of market participants is improved.
Sixth, by optimizing the existing electric power market trading strategy, the overall implementation cost of the electric power system can be continuously reduced, resource allocation is optimized, market stability and economic benefits are improved, and extra cost and risk caused by market fluctuation are reduced.
Seventh, through the use of the power market transaction optimization model, the method is not only applicable to the current power market, but also has strong adaptability, can be expanded to different types of power markets and new energy configuration scenes, has high flexibility and universality, can further cope with changes under different market conditions, and provides a feasible solution.
Based on the above-mentioned power market transaction policy optimization method, one or more embodiments of the present disclosure further provide a power market transaction policy optimization device. The apparatus may include apparatus (including distributed systems), software (applications), modules, plug-ins, servers, clients, etc. that use the methods described in embodiments of the present description in combination with the necessary apparatus to implement the hardware. Based on the same innovative concepts, the embodiments of the present description provide means in one or more embodiments as described in the following embodiments. Because the implementation schemes and methods of the device for solving the problems are similar, the implementation of the device in the embodiments of the present disclosure may refer to the implementation of the foregoing method, and the repetition is omitted. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated. Fig. 3 is a schematic diagram of an electric power market transaction policy optimization device according to an embodiment of the present disclosure. As shown in fig. 3, the power market trading strategy optimization device 300 may include:
The first data obtaining module 301 is configured to obtain historical transaction data of an electric power market, and determine transaction parameters of the electric power market based on the historical transaction data, where the historical transaction data includes transaction data among an electric power generation end, an electric power distribution network and an electric power consumption end.
The model construction module 302 is configured to construct an electric power market trading optimization model based on the determined trading parameters.
A second data obtaining module 303, configured to obtain real-time transaction data and real-time device status data of the electric power market.
The policy optimization module 304 is configured to process the real-time transaction data and the real-time device status data by using the power market transaction optimization model, and optimize a transaction policy of the power market based on a processing result.
In some embodiments of the present description, the power market transaction optimization model may include a power market stabilization function, a resource utilization function, an economic benefit function, a market constraint.
In some embodiments of the present disclosure, when determining the transaction parameters of the power market based on the historical transaction data, the first data obtaining module 301 may be specifically configured to perform clustering processing on the historical transaction data to obtain a plurality of data subsets, determine an association relationship between the plurality of data subsets, and determine the transaction parameters corresponding to each data subset based on the data in each data subset and the association relationship between the plurality of data subsets.
In some embodiments of the present disclosure, the model building module 302 may be specifically configured to determine a transaction scenario type corresponding to each transaction parameter, classify the transaction parameter based on the transaction scenario type of each transaction parameter, build an objective function corresponding to each transaction scenario type based on a classification result, where the transaction scenario type includes a market stabilization scenario, a resource utilization scenario, and an economic benefit scenario, determine a transaction object of the electric market based on the historical transaction data, determine an operation constraint condition of each transaction object and a transaction constraint condition between transaction objects, and build the electric market transaction optimization model based on the objective function, the operation constraint condition, and the transaction constraint condition corresponding to each transaction scenario.
In some embodiments of the present description, the power market transaction optimization model may be represented by the following formula:
Eeconomic＝Σ(Pmarket×Qpower-Cgeneration-Cstorage);
Estability＝-Var(Pmarket)+Balance(Ddemand-Qsupply);
ΣQsupply＝∑Ddemand;
0≤Estorage(t)≤Emax;
Pmin≤Pgeneration≤Pmax;
Wherein Eeconomic represents economic benefits of the electric power market, Pmarket represents electric power price of the electric power market, Qpower represents electric power supply quantity in the electric power market, Cgeneration represents power generation cost of the electric power market, Cstorage represents energy storage cost of the electric power market, Eefficiency represents resource utilization efficiency of the electric power market, Ravailable represents available resource quantity of the electric power market, Estability represents stability of the electric power market, var (Pmarket) represents variance of electric power price of the electric power market, Ddemand and Qsupply respectively represent electric power demand quantity and electric power supply quantity of the electric power market, Estorage (t) represents energy storage electric quantity of the electric power market at time t, Emax represents maximum energy output quantity of energy storage equipment of the electric power market, Pgeneration represents power generation quantity of power generation end of the electric power market, and Pmin and Pmax respectively represent minimum power generation quantity and maximum power generation quantity of power generation equipment of the power generation end.
In some embodiments of the present disclosure, when the power market transaction optimization model is used to process the real-time transaction data and the real-time device state data, the policy optimization module 304 may be specifically configured to preprocess the real-time transaction data and the real-time device state data, where the preprocessing includes data cleaning, outlier detection processing, and data format conversion, input the preprocessed data into the power market transaction optimization model, and solve the power market transaction optimization model by using an optimization algorithm to obtain a target transaction policy.
In some embodiments of the present description, the transaction policy of the electric power market may include an electric power supply policy, an energy storage policy, and an operating parameter of the power generating end device.
The descriptions and functions of the above modules may be understood by referring to the content of the power market transaction policy optimization method, and are not described herein.
The embodiment of the present application further provides an electronic device, as shown in fig. 4, which may include a processor 401 and a memory 402, where the processor 401 and the memory 402 may be connected by a bus or other means, and in fig. 4, the connection is exemplified by a bus.
The processor 401 may be a central processing unit (Central Processing Unit, CPU). The processor 401 may also be other general purpose processors, digital signal processors (Digital SignalProcessor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory 402 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and modules, such as program instructions/modules (the first data acquisition module 301, the model construction module 302, the second data acquisition module 303, and the policy optimization module 304) corresponding to the power market transaction policy optimization method in the embodiment of the invention. The processor 401 executes various functional applications of the processor and data processing, i.e. implements the power market transaction policy optimization method in the above-described method embodiments, by running non-transitory software programs, instructions and modules stored in the memory 402.
The memory 402 may include a storage program area that may store an operating system, application programs required for at least one function, and a storage data area that may store data created by the processor 401, etc. In addition, memory 402 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, such remote memory being connectable to processor 401 through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 402, which when executed by the processor 401, perform the following power market transaction policy optimization method:
the method comprises the steps of obtaining historical transaction data of an electric power market, determining transaction parameters of the electric power market based on the historical transaction data, constructing an electric power market transaction optimization model based on the determined transaction parameters, obtaining real-time transaction data and real-time equipment state data of the electric power market, processing the real-time transaction data and the real-time equipment state data by utilizing the electric power market transaction optimization model, and optimizing a transaction strategy of the electric power market based on a processing result.
The specific details of the electronic device may be correspondingly understood by referring to the corresponding related descriptions and effects in the above method embodiments, which are not repeated herein.
The present specification also provides a computer storage medium storing computer program instructions which, when executed, implement the steps of the above-described power market transaction policy optimization method.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. The storage medium may be a magnetic disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a flash Memory (FlashMemory), a hard disk (HARD DISK DRIVE, abbreviated as HDD), a Solid state disk (Solid-state disk STATE DRIVE, SSD), or the like, and may further include a combination of the above types of memories.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are referred to each other, and each embodiment is mainly described as different from other embodiments.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
From the above description of embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of some parts of the various embodiments of the present application.
The application is operational with numerous general purpose or special purpose computer system environments or configurations. Such as a personal computer, a server computer, a hand-held or portable device, a tablet device, a multiprocessor system, a microprocessor-based system, a set top box, a programmable consumer electronics, a network PC, a minicomputer, a mainframe computer, a distributed computing environment that includes any of the above systems or devices, and the like.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
While the present application has been described by way of embodiments, those of ordinary skill in the art will recognize that there are many variations and modifications of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and modifications as do not depart from the spirit of the application.