274 results about "Ancillary service" patented technology

The invention discloses a flexible load multi-time scale coordination control method for a novel power system, and the method comprises the specific steps: collecting millisecond, minute and hour level flexible loads and environment data, and constructing a multi-time scale load response model; a millisecond-level model and a second-level model are utilized, a real-time operation state is combined, a second-level adjusting instruction is generated, and an execution result is fed back; based on minute-to-hour level prediction data, integrating adjustable load, distributed energy and energy storage constraints, optimizing a load operation plan in a rolling manner, and coordinating source-network load-storage combined operation; and making a long-term load operation plan in combination with day-level and above prediction data, and executing deviation self-adaptive correction according to short and medium periods. According to the method, hierarchical control and collaborative optimization of the adjustable load under different time scales from millisecond level to day level are realized, and the operation stability, the adjustment flexibility and the auxiliary service capability of the novel power system under the high-proportion new energy access condition are remarkably improved.

The invention provides a virtual power plant-oriented multi-objective optimization model construction and regulation method and system, and is applied to the technical field of virtual power plant operation optimization. The method comprises the steps of dividing distributed resources in a virtual power plant into continuous adjustment type resources, graded adjustment type resources and switch adjustment type resources based on adjustment characteristics of the distributed resources in the virtual power plant; the aggregation cost and the aggregation income of various adjustment resources aggregated in the process that the virtual power plant aggregator participates in the power market auxiliary service are considered, and a multi-objective optimization objective function is constructed by minimizing the aggregation cost and maximizing the aggregation income; and determining constraint conditions based on the adjustment capability and the adjustment rule constraint of various adjustment resources, the production safety constraint and the power grid operation constraint so as to construct a virtual power plant-oriented multi-target optimization model. The method solves the problems that the participation mode cannot be optimized and the ability of an aggregator to participate in the auxiliary service is relatively low due to the fact that the transaction model of the virtual power plant participating in the auxiliary service market is insufficient in fineness.

The invention discloses an energy storage system multi-scene optimization scheduling method and device, equipment, a medium and a product, and relates to the field of model optimization, and the method comprises the steps: obtaining an initial decision variable and a constraint term which are used for training a scheduling model; obtaining a prediction data set; based on the predicted photovoltaic output in the prediction data set, the first predicted electricity price set, the second predicted electricity price set, the initial decision variable and the constraint term, generating the predicted total income of the electric energy market and the auxiliary service market; on the basis of the predicted total income, updating the initial decision variable so as to optimize the scheduling model, and obtaining an optimized target scheduling model; based on the optimal decision variable of the target scheduling model, adjusting a charging and discharging control strategy of the energy storage system; according to the predicted electricity price and total income, the charging and discharging strategy of the energy storage system is dynamically adjusted, so that the energy storage system can participate in an electric energy market and an auxiliary service market at the same time, and the economic benefit of the whole electric power market is maximized.

The invention belongs to the technical field of virtual power plant resource scheduling, and discloses a virtual power plant flexible resource calling method and device based on a power grid visual angle and a medium, so as to solve the problems that the contribution of flexible resources to the state change of a power distribution network is ignored, the performance evaluation is inaccurate, the response result is not in place, and the response intention is not clear. The method comprises the following steps: acquiring real-time measurement data of each flexible resource of each virtual power plant and grid-connected point topological structure data, and establishing a power grid view angle; obtaining the response capability and historical response data of each flexible resource of each virtual power plant to calculate the contribution degree to a power grid node, the excitation response intention, the response qualification rate, the response rate, the response preparation time and the response duration to construct a user portrait; and obtaining a response demand or an auxiliary service interaction adjustment demand generated by the power grid, and calling flexible resources to execute an interaction response in combination with the constructed power grid view angle and the user portrait so as to meet whole-network and local real-time demands of the power grid, thereby completing flexible resource calling.

The invention discloses a park source network load storage multi-agent transaction mechanism in a multi-element market, and relates to the technical field of power market transaction mechanisms. The method comprises the following steps: determining a game relationship between a park source network load storage multi-agent and each benefit agent; depicting an electric energy market transaction framework, an auxiliary service market transaction framework and a multi-market collaborative transaction framework; based on medium and long term market transactions, spot market transactions and auxiliary service market transactions, a park source network load storage multi-subject transaction mechanism under multiple markets is constructed. According to the park source network load storage multi-agent transaction mechanism under the multi-element market provided by the invention, multiple agents are introduced to participate in multi-element market transaction and collaborative operation, so that diversified supply and consumption of electric power in the park are realized, and the competitiveness and flexibility of the electric power market are promoted; meanwhile, through information interaction and collaborative transaction, balance and optimization of power supply and demand in the park are realized, new energy is utilized to the greatest extent, and sustainability and stability of a power system are improved.

The invention discloses a layered and distributed cooperative scheduling method and system for a smart rod cluster and a computer program product, and belongs to the technical field of power system energy optimization scheduling, and the method comprises the steps: building a day-ahead scheduling optimization model that smart rod cluster aggregation resources participate in an electric energy market and a peak regulation auxiliary service market at the same time; according to the day-ahead scheduling optimization model, proposing a layered and distributed cooperative scheduling method based on an alternating direction multiplier method scaling form; based on a hierarchical distributed collaborative scheduling method, respectively solving an upper-layer smart rod cluster operator management platform sub-problem and a lower-layer smart rod cluster sub-problem; and after the sub-problems of the upper layer and the lower layer are solved, an aggregation power allocation method considering the flexible resource feasible region of the multi-type smart rod cluster is proposed, and the aggregation power of the smart rod cluster is allocated to each smart rod. Based on the alternating direction multiplier method, the scheduling problem of a large-scale resource cluster can be efficiently solved, higher economy is achieved, and privacy of various resource users is better protected.

The invention relates to an electric vehicle layered optimization regulation and control method considering multi-subject interests. The method comprises the following steps: firstly, constructing a market clearing model for an electric power peak regulation auxiliary service based on a power grid operator, constructing a regulation and control bidding model considering a bidding success rate based on an electric vehicle aggregator, and constructing a charging response model considering an incentive price based on a user; then, based on the market clearing model, the regulation and control bidding model and the charging response model, constructing a regulation and control model of a three-layer regulation and control framework of a power grid operator, an electric vehicle aggregator and a user; and finally, predicting and solving the regulation and control model based on the three-layer regulation and control architecture to obtain reference power, jointly constructing an objective function based on the reference power and actual power, performing deviation correction, and finishing optimization regulation and control of the electric vehicle when the objective function is minimum. The power peak regulation of the power grid side is realized, the operation cost of the power grid is reduced, the benefit of an electric vehicle aggregator is maximized, and the charging requirement of a user is met.

The invention provides an operation optimization method, device and equipment of a pumped storage power station and a storage medium, and relates to the technical field of operation optimization of a power system. The method comprises the following steps: taking the maximum expected revenue and the minimum participation risk when the pumped storage power station participates in an electric energy market and a frequency modulation auxiliary service market as an optimization target, and according to the expected revenue and the participation risk of the pumped storage power station at each moment in an optimization time period and a target function of the next moment corresponding to each moment, optimizing the optimization time period of the pumped storage power station; target functions of the pumped storage power station at all moments in the optimization time period are established respectively; and a constraint condition is established, iterative solution is performed on the target function at each moment based on the constraint condition, and optimal operation strategies of the pumped storage power station at different moments in the optimization time period are obtained, so that operation of the pumped storage power station is controlled according to the optimal operation strategies in the optimization time period. The operation strategy of the pumped storage power station can be prevented from falling into local optimum.

The invention discloses a decision-making method for a light storage and charging multifunctional charging station to participate in peak regulation auxiliary service based on reinforcement learning, and relates to the field of power system analysis and calculation. Establishing a cost objective function through comprehensive analysis of charging service income, electricity purchasing and selling cost, peak regulation response income and peak regulation deviation punishment cost; obtaining a peak regulation operation constraint condition based on confluence analysis of an electric power and electric quantity balance constraint, a peak regulation capacity constraint, an energy storage system charging and discharging constraint, an electric vehicle charging constraint and a spinning reserve capacity constraint; and establishing a peak regulation auxiliary service decision model according to the cost objective function and the peak regulation operation constraint condition, and training through a reinforcement learning algorithm to obtain an energy storage charging and discharging strategy. According to the method, cost-benefit factors such as system operation and maintenance cost and carbon emission reduction benefit are considered, and optimization of peak regulation cost benefit can be realized when the light storage and charging multifunctional charging station participates in peak regulation auxiliary service.

The invention relates to a multi-type energy storage system capacity configuration method suitable for peak regulation auxiliary service, and the method gives full play to the characteristics of high flexibility and wide application range of multi-type energy storage through the application of multi-type energy storage in multiple time scales. Factors such as economy, safety and peak regulation effect are considered to perform multi-target optimization analysis on the multi-type energy storage configuration, and the economic cost of the multi-type energy storage configuration is reduced while the peak regulation effect is ensured; and scheduling calculation is carried out through an actual application scene, so that the accuracy and credibility of multi-type energy storage capacity configuration are improved, and the method has important guiding significance and engineering application value for multi-type energy storage power capacity configuration for peak regulation.

The invention discloses an energy storage electricity market bidding method, device and equipment and a storage medium, and relates to the field of energy storage optimization scheduling, and the method comprises the steps: an independent energy storage bidding decision model sends first quotation data to an electricity market clearing model; the electricity market clearing model determines the total electricity purchase cost based on the first quotation data and the second quotation data of each conventional unit in the plurality of conventional units; the power market clearing model determines bid-winning data of the independent energy storage bidding decision-making model based on the total power purchase cost, and sends the bid-winning data to the independent energy storage bidding decision-making model; the independent energy storage bidding decision model determines the total income of the electric energy market and the frequency modulation auxiliary service market based on the bid winning data; and the independent energy storage bidding decision-making model optimizes the first quotation data based on the total income, and sends the optimized first quotation data to the electricity market clearing model, and the optimization is ended until the reduction of the total electricity purchase cost and the increase of the total income are both smaller than a preset threshold value or the number of iterations is reached.

The invention provides an operation optimization method and system for an energy storage participation auxiliary service market, and the method comprises the steps: determining an energy storage performance aging factor, and defining an energy storage aging time function according to the energy storage performance aging factor; the energy storage performance aging factors comprise calendar aging and cycle aging; based on the energy storage aging time function, constructing an intra-day operation model taking energy storage expected life maximization and intra-day benefit maximization as objective functions; and solving the intra-day operation model by adopting a multi-agent algorithm of a non-adversarial game to obtain an optimal strategy of energy storage intra-day operation. According to the invention, the relation between the energy storage aging characteristic and the working characteristic is introduced to carry out the energy storage intra-day operation decision, the economic benefit can be truly evaluated, the energy efficiency of energy storage and the stability of scheduling are considered, and the operation maintenance level is greatly improved.

The invention relates to a revenue calculation and resource scheduling method and device for participation of a power load aggregator in an auxiliary service market, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring power supply consumption cost and discharge cost corresponding to each load aggregation main body; according to each power supply consumption cost and each charging cost, determining an electric quantity compensation cost of each load aggregation main body corresponding to a target time period; and according to the electric quantity compensation value, the performance compensation value and the power supply amount corresponding to the target time period, determining a target electric quantity compensation income of each load aggregation main body corresponding to the target time period. By adopting the method, the accuracy of the cost compensation calculation method for the auxiliary service of the electricity market can be improved.

A method, system and apparatus are disclosed. A management node configured to communicate with a power grid operator and with a plurality of RBSs is provided. Each of the plurality of RBSs is configured to be switchable between power from a power grid and power from a respective plurality of backup battery units associated with the RBS. The management node is configured to: determine a primary subset of the plurality of RBSs to participate in at least one FCR event occurring over a predefined time interval, and determine a standby subset of the plurality of RBSs that are each configured to participate in the at least one FCR event in place of a failure of a respective one of the primary subset of the plurality of RBSs.

Dust tolerant robotic interfaces and systems, methods, and devices for a dust tolerant robotic interface are provided. A dust tolerant electromechanical interface includes a parent module and a child module. The parent module includes a parent mating surface, a mechanical alignment feature, a rigidizing module, and an auxiliary services module. The child module includes a child mating surface, a mechanical alignment target, a rigidizing target, and an auxiliary services reception module. The interface includes unmated and mated and rigidized configurations. In the mated and rigidized configuration, the parent mating surface and child mating surface are substantially parallel and within a separation distance, the mechanical alignment feature engages the mechanical alignment target, the rigidizing module engages the rigidizing target, the auxiliary services reception module engages the auxiliary services reception module, and the rigidizing module is enabled, fixing the parent module to the child module. Auxiliary services may include any one or more of electrical power, mechanical torque, and data.

The invention belongs to the technical field of power system operation scheduling, and particularly relates to an optimal scheduling method for a source-grid-load-storage integrated project, which comprises the following steps of: preprocessing historical data of renewable energy output power and load in a given data sequence period in combination with a source-grid-load-storage area; a seasonal autoregressive integral moving average model and a grey prediction model GM (1, 1) for prediction are constructed and trained according to the model, and then the two kinds of prediction data are subjected to weighted average to obtain the final prediction result of the output power and the load of the renewable energy sources in the future; constructing a source network load storage collaborative scheduling model considering the inverter to provide the auxiliary service, and inputting the prediction data into the collaborative scheduling model to obtain an initial scheduling scheme; and finally, scheduling distribution network and energy optimization in source network load storage by adopting an improved tree species optimization algorithm. According to the invention, distribution network security scheduling is optimized to minimize the total cost of market transaction, so that the economical efficiency of a source-network-load-storage integrated project is improved.

In one aspect, a method of managing a participation of a virtual power plant (VPP) comprising a plurality of electric vehicle supply equipment (EVSE) in an ancillary service market for an electric grid is provided. The method comprises generating a bid for participating in the ancillary service marked over a time period, transmitting the bid to the ancillary service market, receiving a response from the ancillary service market indicating that the bid was accepted, and identifying a request from the ancillary service market for the VPP to provide grid support to the electric grid during the time period. The method further comprises generating, based on the bid and in response to the request, at least one charging profile for one or more of the EVSEs, and providing the at least one charging profile to the one or more of the EVSEs to implement the request for grid support.

The invention provides an energy storage participation electric energy and frequency modulation auxiliary service market joint clearing method. The method comprises the steps that system operation data and unit load data are acquired; constructing a model objective function and a model constraint; constructing an electric energy and frequency modulation auxiliary service market joint clearing model based on the model objective function and the model constraint; performing capacity safety check on the electric energy and frequency modulation auxiliary service market combined clearing model, and adjusting the combined clearing model based on a check result to generate an adjusted combined clearing model; and repeating the steps until the verification is passed, and outputting clearing results of the electric energy market and the frequency modulation auxiliary service market. The frequency modulation capacity constraint and the energy storage constraint are added into the combined clearing model, the influence of the output plan of the electric energy market and the frequency modulation auxiliary service action on the energy storage SOC can be considered, the execution effectiveness of the energy storage SOC is ensured, meanwhile, the influence on the stability of the power system can be prevented through capacity safety checking, and the feasibility of the output result is improved.

The invention discloses a dynamic pricing method and device for providing auxiliary service adjustment by multiple subjects, and relates to the technical field of dynamic pricing, and the method comprises the following steps: obtaining historical data of all units in a power market, and carrying out the preprocessing of the historical data to obtain first data; constructing an auxiliary service adjustment dynamic pricing mechanism model based on a system dynamics theory in combination with the first data; judging whether adjustment is needed or not according to the operation condition of the model, and if adjustment is not needed, performing dynamic pricing calculation; if adjustment is needed, influence parameters are obtained based on sensitivity test analysis, coefficients on an influence parameter link are preferentially adjusted, and a logic relation is reconstructed to carry out feedback correction on the model; and calculating according to the corrected model to obtain a correlation adjustment reasonable cost reference value. And dynamic adjustment of the auxiliary service price is realized through the established causal relationship graph and the like. Through real-time data acquisition and updating, the model can dynamically calculate the pricing of the auxiliary service according to the constructed flow graph, and a scientific basis is provided for pricing.

The invention discloses an environment deployment method and system of a cloud native electric power auxiliary service market system, and belongs to the technical field of electric power system informatization. The method comprises the following steps: acquiring test environment configuration information and a system running state; constructing an infrastructure and deploying a system service component according to the test environment configuration information, and generating a test environment consistent with the production environment; testing the test case in the test environment to obtain a test result; verifying the system operation state and the test result according to a preset verification rule to obtain a test report; the test report comprises a test success report and a test failure report; when the test success report exists and the test coverage is in accordance with the preset range, allowing the system to be deployed to the production environment; and when the test report does not exist, the test failure report exists or the test coverage does not meet the preset requirement, the system is prohibited from being deployed to the production environment. The problems that the test environment generation efficiency is low and deployment verification is not strict can be solved.

The invention provides an auxiliary service-based polymer dynamic interaction method and a related device. The method comprises the following steps of: obtaining energy efficiency data and auxiliary service data corresponding to a target polymer; the target polymer comprises a target sets; constructing a model according to the energy efficiency data and the auxiliary service data to obtain a dynamic interaction model; obtaining power grid required power of a target power grid corresponding to the target polymer in a preset first control period; calculating according to the power grid demand power through the dynamic interaction model to obtain a reference power regulating variable; and performing power adjustment on the target polymer according to the reference power adjustment quantity to ensure that the total power of the target polymer and the power grid demand power are in a power balance state. By interactively adjusting the power of the target polymer, the load change of the power grid can be responded in real time, and the power balance between the user and the power grid is coordinated.

Method and system for creating and implementing a cleaning-in-place installation cleaning process for a process installation, wherein an installation to be cleaned is represented as process modules that each include an item of self-description information with respect to the respective cleaning properties and that are each configured to transmit the self-description information, where the system has at least one auxiliary module that provides auxiliary services for the installation cleaning process, and a process planning unit that has an item of cleaning process information having cleaning steps needed at least for the installation cleaning process and model descriptions of cleaning efficiency for each cleaning step, and the process planning unit determines a cleaning schedule in a partially automated manner on a system-specific basis based on the cleaning process information supplemented with self-description information relating to the process modules and taking into account information relating to the at least one auxiliary module.

The present application relates to devices and components including apparatus, systems, and methods for carrier aggregation operations in wireless communication systems.

The invention discloses a unit output adjustment method and device considering various auxiliary services, and a medium. The method comprises the following steps: establishing a new energy time-sharing capacity market model related to a new energy unit and a flexible resource spot market model related to a flexible resource unit according to operation data of a power system; and solving the model, respectively obtaining the output of the new energy unit and the output of the flexible resource unit, and adjusting the power system according to the output of the new energy unit and the output of the flexible resource unit. According to the unit output adjustment method and device considering various auxiliary services and the medium provided by the invention, by establishing the new energy time-sharing capacity market model, the output condition of a new energy unit can be simulated and predicted more accurately; by introducing the flexible resource unit, the flexibility of the power system can be remarkably improved, and the problems that the resource configuration of the power system is too single and the load is difficult to respond to the fluctuation of new energy in time can be solved.

The invention discloses a virtual power plant game transaction rapid convergence method and system, a storage medium and equipment. The method comprises the following steps: constructing a reference auxiliary service electricity price coefficient including response efficiency, response quality and response reliability of a virtual power plant based on response information; constructing a basic electricity price based on the real-time supply and demand condition and the real-time electricity price of the power grid; constructing a virtual power plant game transaction model based on the reference auxiliary service electricity price coefficient and the basic electricity price; and finally, carrying out iterative convergence on the virtual power plant game transaction model based on the reference auxiliary service electricity price through a Markov decision process and a reinforcement learning algorithm. According to the method, the reference auxiliary service electricity price is constructed in the game transaction model between the virtual power plant and the auxiliary service market, so that the deviation between the initial electricity price and the final market clearing electricity price can be effectively reduced, the number of times of game iteration is reduced, the convergence speed of the game is improved, and the market clearing efficiency is improved.

The invention discloses a collaborative charging optimization strategy for an electric vehicle aggregator EVA participating in a day-ahead energy market and a frequency modulation auxiliary service market, belongs to the technical field of energy internet regulation and control, considers the influence of multi-stage income evaluation and an actual frequency modulation effect on income, meets the diversified charging requirements of users, and improves the energy utilization rate of the users. A collaborative charging optimization strategy that EVA participates in a day-ahead energy market and a frequency modulation auxiliary service market is provided, and a control system that EVA participates in peak regulation and frequency modulation of a power grid is constructed to analyze a frequency response effect. In the influence of the actual frequency modulation effect on earnings, in order to improve the frequency modulation quality in the power market, the performance of frequency modulation resources is assessed according to the actual frequency modulation effect, the resources with high scores obtain high earnings, and the frequency modulation earnings are paid according to the assessment result. According to the method, the influence of the frequency modulation effect on earnings is considered, and the value and benefit brought by EVA charging control to a power grid are reflected by researching the charging control of aggregators for aggregating the electric vehicles and realizing marketization transaction.

The invention provides a benefit evaluation method for participation of energy storage equipment in electric power auxiliary service, and the method comprises the steps: extracting a peak regulation service emergency degree and a frequency modulation response real-time requirement, recognizing the priority order of each service response, distributing mixed output data to a peak regulation part and a frequency modulation part according to the priority, and obtaining a power share after preliminary decomposition; standby loss data of standby standby are obtained and matched with the charging and discharging power overlapping part, if the loss data are matched with non-allocation residues in the power share obtained after preliminary decomposition, the residues are assigned to the standby standby, and complete service type power allocation is obtained; and extracting a revenue adjustment coefficient from the allocated revenue value, identifying an allocation weight of the revenue adjustment coefficient between the services, and if a deviation exceeds a threshold value, reallocating to obtain an optimized accurate allocation result.

The embodiment of the invention discloses a data processing method, device and equipment, and the method comprises the steps: obtaining an execution request of a core service in a target business, and when it is detected that other services except the core service need to be executed in the execution process of the core service, executing the execution request of the core service; according to the method, verification parameters and auxiliary processing demand data used for calling auxiliary services are obtained, then, target auxiliary services matched with the verification parameters can be searched in an auxiliary service database based on the verification parameters, the target auxiliary services are called, and the auxiliary services are called through the target auxiliary services. And executing auxiliary processing corresponding to the auxiliary service calling request based on the auxiliary processing demand data to obtain an auxiliary processing result corresponding to the auxiliary service calling request, and finally, continuing to execute the core service based on the auxiliary processing result corresponding to the auxiliary service calling request.

The invention discloses a clearing model and a pricing mechanism of an electric energy and auxiliary service combined market under strong uncertainty. A fuzzy set of system uncertainty power, a combined market clearing model considering the uncertainty power and an electric power combined market price system are constructed in sequence. According to the invention, node marginal prices of electric energy, standby, frequency modulation and uncertain power are obtained based on a marginal pricing principle. Price excitation is provided for flexible resources through standby and frequency-modulated node marginal prices, and an uncertain market subject is generated through node marginal price punishment of uncertain power. In the aspect of solution of a clearing model, distributed robust bilateral opportunity constraints are converted into mixed integer second-order cone programming through convex reconstruction. The method aims at enabling the uncertain power punishment cost amount generated by a new energy station to flow to a traditional generator set and an energy storage enterprise which provide standby capacity and frequency modulation power through a market pricing mechanism, and the actual application value of an uncertain power electricity price mechanism is verified.

The invention relates to the technical field of power system auxiliary services, in particular to a multi-agent participated user group dynamic combination and economic optimization method in power auxiliary services, which comprises the following steps of: constructing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution ', formulating an incentive price through a Stackelberg game between upper-layer ISO (International Standard Organization) and LAs (Local Assisted Services), and establishing a multi-agent collaborative model of'upper-layer scheduling-middle-layer aggregation-lower-layer execution'; the evolutionary game between the lower-layer LAs and the user realizes user strategy interaction; introducing maximum profit power reduction and power transfer potential indexes, and performing user group preliminary screening in combination with a multi-energy profit map; and outputting an optimal user group combination scheme through iterative solution and joint uncertainty analysis. The invention further provides an alternative scheme, and dynamic combination optimization is realized by utilizing the user potential portrait and a reinforcement learning mechanism. The method can effectively improve the economy, flexibility and robustness of the power auxiliary service, and gives consideration to the comfort of the user and the adjustment demands of the system.