96 results about "Purchase cost" patented technology

The invention discloses an enterprise purchase collaborative management method and system based on a cloud platform, and relates to the technical field of cloud platform and supply chain management, and the method comprises the steps: collecting multi-factor demand prediction model data; predicting a purchase demand and performing optimization; and executing purchasing based on the demand prediction and optimization result. According to the enterprise purchase collaborative management method based on the cloud platform, purchase demand prediction is carried out through a multi-factor dynamic time sequence model, high precision and high adaptability of demand prediction are realized in combination with a dynamic weight adjustment mechanism and adaptive optimization of model parameters, a purchase demand trend chart and influence factor analysis can be provided, and the enterprise purchase collaborative management method based on the cloud platform can be applied to enterprise purchase collaborative management. According to the method and the system, the enterprise is helped to make a purchase plan in advance, the purchase cost, the inventory holding cost and the stockout risk are balanced by taking the minimization of the total purchase cost as the target through the demand-inventory joint optimization model, and the method and the system have better effects in the aspects of accuracy, controllability and stability of enterprise purchase.

The invention provides a bidding method for virtual power plants participating in a joint market based on a master-slave non-cooperative game. The bidding method comprises the following steps: constructing a double-layer game framework in which multiple virtual power plants participate in a day-ahead joint market; carrying out Wasserstein distance uncertainty modeling on the wind power, the photovoltaic power and the electric load power; based on the multi-source load power uncertainty set, an upper-layer multi-virtual power plant two-stage distribution robust bidding model is constructed, and the bidding model comprises a first objective function with the minimum sum of pre-scheduling cost for making a bidding strategy in a day-ahead stage and rescheduling cost expectation in an intra-day stage and a corresponding first class of constraint conditions; constructing a lower-layer joint market clearing model; the clearing model comprises a second objective function with the minimum day-ahead electricity-peak regulation-hydrogen combined market energy purchase cost and a corresponding second class of constraint conditions; performing equivalent conversion on the bidding model and the clearing model; and solving the converted bidding model and the converted clearing model to obtain a bidding strategy.

The invention discloses a cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, and relates to the technical field of data processing, and the system comprises a data processing module which obtains real-time data of purchase cost, exchange rate fluctuation, storage cost and logistics cost, cleans and standardizes multi-source heterogeneous data, obtains a cost data set of a unified format, and stores the cost data set of the unified format; the trend analysis module is used for calculating the change trend of the purchase cost, the exchange rate fluctuation, the storage cost and the logistics cost in the time dimension according to the cost data set in the unified format to obtain the dynamic weight of each cost factor, and the inventory pricing module is used for calculating the real-time comprehensive cost of each commodity by adopting a weighted average algorithm according to the dynamic weight of each cost factor and calculating the real-time comprehensive cost of each commodity. A dynamic inventory pricing result is obtained; according to the cross-border e-commerce multi-platform order intelligent scheduling and inventory collaborative management system, the problems of pricing lag and profit loss caused by the fact that a traditional inventory pricing method cannot respond to cost fluctuation in real time are solved.

The invention relates to the technical field of purchase management, in particular to an intelligent purchase management method and system, and the method comprises the steps: data collection, data processing and storage, demand prediction, supplier management, purchase plan generation, order tracking and management, data analysis and reporting and the like. The system is composed of a data acquisition module, a data processing module, a demand prediction module, a supplier management module, a purchase plan generation module, an order management module, a data analysis and report module and a user interface module. According to the invention, automation and intellectualization of the purchasing process can be realized, the purchasing cost is reduced, the supply chain management capability is enhanced, and the overall competitiveness is improved.

The invention discloses a data center energy consumption optimization method based on electricity price and load prediction, and relates to the technical field of scheduling optimization of a demand side, and the method comprises the steps: collecting parameters related to the energy consumption of a data center, constructing a data center energy consumption model based on workload transfer, proposing an electricity price and workload prediction model based on an LSTM neural network, and optimizing the energy consumption of the data center. The method comprises the following steps: constructing a data center load optimization scheduling decision model in a market environment by taking minimization of power purchase cost as an objective function and considering constraint conditions, and performing simulation verification on the model by utilizing an IEEE30 node system to obtain the electricity price of a prediction day, the arrived workload, the workload scheduling condition and the power purchase cost. According to the data center energy consumption optimization method based on the electricity price and load prediction, through introduction of the LSTM model, short-term and long-term fluctuation of the electricity price can be accurately predicted.

The invention discloses an auxiliary decision-making method and system for regional coal market purchase, and belongs to the technical field of coal market purchase decision-making, and the method comprises the steps: building model input parameters through collecting the data of a fire coal demand plan of a power plant, the maximum supply of a supplier, a coal quality index, a purchase price and a transportation cost; constructing a mixed integer linear programming model taking the minimization of the total purchase cost as a target function, and setting a demand satisfaction constraint, a supply capability constraint and a coal quality standard reaching constraint; solving the mixed integer linear programming model by adopting a two-stage approximation algorithm; and converting the obtained approximate optimal solution into a visual purchasing scheme chart and report, and supporting a user to dynamically adjust the purchasing scheme through an interactive interface to generate a final purchasing decision. According to the invention, on the basis of comprehensively considering the coal quality, the purchase cost, the transportation mode and the supplier capability, the coal purchase plan is optimized, the cost minimization is realized, and the overall quality of coal purchase is improved.

The invention provides a transaction and scheduling optimization method and system for a virtual power plant participating in a spot market, relates to the technical field of energy systems, and solves the problem of electricity purchase cost minimization of the virtual power plant participating in spot transaction and the problem of how to report and regulate adjustable resources in the virtual power plant containing multiple distributed energy main bodies. The method comprises the following steps: acquiring first data and second data; according to the spot transaction price, the static data, the first data and the second data, constructing an optimization target for minimizing the overall power purchase cost of the virtual power plant, and setting a comprehensive power constraint condition and an energy storage system constraint condition; and solving through a linear programming solver to obtain an optimal spot declaration strategy and a regulation and control strategy of corresponding equipment. The method is used in the virtual power plant transaction and scheduling optimization process, the declaration strategy of the electric power spot market is considered, the electric quantity of the real-time market is adjusted through adjustable resources such as adjustable equipment and energy storage equipment on the user side, and the electricity purchase cost can be reduced and the marketization risk can be controlled.

The invention relates to a transaction mechanism and a clearing method for an electric power alternative spot market, and aims to solve the adjustment problem caused by access of high-proportion renewable energy in a novel electric power system. The method comprises the following steps: firstly, defining volume adjustment commodities and a transaction mechanism thereof, generating and correcting a system adjustment demand curve based on an adjustment demand curve reported by a market subject, collecting the volume adjustment quotation of each market subject to construct a clearing model, carrying out clearing by adopting an equal volume adjustment increment criterion, and determining a bid-winning volume. And dynamically adjusting the adjustment demand of the system through multiple rounds of adjustment transactions until the adjustment demand converges to zero. The method further comprises an external quantity adjustment price determination method and a transaction surplus allocation mechanism, and can eliminate the negative extrinsic property of the adjustment capability, realize the marketization configuration of the adjustment capability, improve the market operation transparency and predictability, reduce the total electricity purchase cost of the system, simplify the market clearing process, and improve the user experience. The method is suitable for a novel power system with high-proportion renewable energy access.

The invention discloses an energy storage participation electric power spot-frequency modulation market joint bidding clearing method based on a DDPG algorithm, and the method comprises the steps: building a quotation decision-making layer model with the maximum profit of a power generator as a first target, and the profit of the power generator comprises the profit of a thermal power unit, the profit of a new energy unit, and the profit of an energy storage unit; constructing a market clearing layer model by taking the minimum total purchase cost of the electric energy and the frequency modulation auxiliary service as a second target, wherein the total purchase cost comprises the purchase cost of a thermal power generating unit, the purchase cost of a new energy unit and the purchase cost of an energy storage unit; obtaining a joint bidding clearing double-layer model based on the quotation decision-making layer model and the market clearing layer model; and solving the joint bidding clearing double-layer model by using a DDPG algorithm to obtain an optimal quotation and quantity reporting strategy of each market subject in the electric power spot market and the frequency modulation market. According to the invention, an optimal quotation and quantity reporting strategy can be provided for each market subject including the energy storage unit to participate in electric power spot-frequency modulation market joint clearing, so that reasonable allocation of market resources is realized.

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 discloses a power generation alliance electricity-carbon-green certificate market bidding decision-making method based on an MADDPG algorithm. The power generation alliance comprises a renewable energy unit and a conventional thermal power unit, and the market further comprises a traditional thermal power generation body and a wind and light new energy power generation body. The method comprises the following steps: constructing a quotation decision-making model of each market subject participating in the electricity-carbon-green certificate market based on the selling income of each market subject in the electricity-carbon-green certificate market, and taking the quotation decision-making model as a decision-making model; constructing an electricity market day-ahead clearing model, a carbon market clearing model and a green certificate market clearing model as coupling clearing models by taking minimum electricity purchase cost, carbon market welfare maximization and green certificate market welfare maximization of the electricity market as targets; constructing a double-layer coupling transaction model by taking the decision model as an upper-layer model and the coupling clearing model as a lower-layer model; and adopting an MADDPG algorithm to solve the double-layer coupling transaction model, and determining the middle bid price, the middle bid amount and the total income of each market subject in the electricity market, the carbon market and the green certificate market.

The invention discloses an electrolytic aluminum enterprise power purchase strategy optimization method and device based on an intelligent contract, a medium and equipment, and the method comprises the steps: enabling an electrolytic aluminum enterprise to generate a plurality of purchase strategies based on the minimization of the power purchase cost as a target, and enabling a power market operator to generate a plurality of purchase strategies based on the maximization of the operation profit as a target; and the strategies are comprehensively sorted through an intelligent contract, so that an optimal purchase strategy combination is determined. The electrolytic aluminum enterprise adjusts electric power purchase according to the target power of the combination, and it is ensured that electric power use in the production process is more efficient and economical.

The invention relates to the technical field of power markets and distributed energy regulation and control, in particular to a cross-market joint clearing and safety checking method, device, equipment and program product. The method comprises the steps of obtaining cost information and income information of power generation of each target aggregator; taking the cost information of each node as the input of a power purchase cost planning layer in the game model, taking the income information of an operator as the input of an income planning layer in the game model, and solving the game model to obtain a scheduling plan; checking the scheduling plan by using the security constraint condition, and determining the scheduling plan as a target plan in the clearing process under the condition that the scheduling plan meets the security constraint condition; wherein the safety constraint condition comprises constraint on power transmission section out-of-limit and constraint on deviation cost of power supply quantity. By adopting the method, the safety in the combined clearing process can be improved.

The invention discloses a power plant fire coal matching and purchasing decision-making method and system, relates to the technical field of power plant fuel management, and aims to solve the problems of high purchasing cost and low decision-making efficiency caused by low coal source and unit demand matching degree and dependence on artificial experience of an existing power plant fire coal purchasing decision-making method. The method comprises the following steps: acquiring multi-source heterogeneous data; processing the multi-source heterogeneous data to generate a standardized feature data set; inputting the standardized feature data set into an AI fusion analysis model fusing a gradient boosting tree and an attention mechanism, and outputting a cost prediction result and a key index weight; and adopting an improved hybrid particle swarm optimization algorithm, and iteratively determining an optimal solution of the four-dimensional variable of the multi-objective function through an inertia weight dynamic adjustment strategy to obtain a target purchasing scheme. The power plant fire coal matching and purchasing decision-making method provided by the invention is used for improving the matching degree of the coal source and the unit demand in the fire coal purchasing decision-making, reducing the purchasing cost and improving the decision-making efficiency.

The invention relates to the technical field of data processing, in particular to a purchase order generation method and device, equipment and a storage medium, and the method comprises the steps: processing and preprocessing historical purchase data to train a purchase quantity prediction model; obtaining to-be-predicted information and inputting the to-be-predicted information into the purchase quantity prediction model to obtain a demand prediction result; analyzing the real-time price trend to obtain a trend analysis result; generating a purchase quantity and a purchase budget in combination with the real-time inventory information, the demand prediction result and the trend analysis result; evaluating supplier information, determining a criterion and a weight, and calculating a comprehensive evaluation score corresponding to a supplier; ranking suppliers according to the comprehensive evaluation scores, and generating a purchase order based on the purchase quantity, the purchase budget and a supplier ranking result; according to the method disclosed by the invention, the real-time purchase demand and the price fluctuation trend are integrated, and the corresponding purchase strategy is formulated, so that the efficiency of the purchase activity can be remarkably improved, the purchase cost is reduced, and the competitiveness of an enterprise in the market is enhanced.

This invention relates to the field of power system demand response technology, and provides a user demand response method based on master-slave game theory under cost control constraints. The method includes: establishing a demand response model using master-slave game theory, where electricity retailers design time-of-use pricing strategies, and electricity users respond based on a convex utility function; the utility function includes a quantification term for electricity usage satisfaction and a calculation term for electricity costs; the electricity retailers' revenue function includes four functions: user electricity costs, long-term market electricity purchase costs, current market electricity purchase costs, and conditional risk value, coupled with the characteristics of the electricity spot market and risk management requirements; proving the existence of a Nash equilibrium solution using backward induction; and optimizing the strategy based on a slime mold optimization algorithm. This invention provides important support for electricity retailers to formulate flexible pricing mechanisms and tap demand-side resources in terms of cost control for users with high response potential and optimization of peak-valley load differences.

The invention discloses a commodity information display method and device, a commodity information acquisition method and device, electronic equipment and a storage medium, in the commodity information display method, to-be-combined commodity information and combined commodity summary information are acquired, and then a to-be-combined commodity selection trigger control and a plurality of combined commodity selection trigger controls can be displayed on a to-be-combined commodity detail page. The combined commodity selection trigger control at least displays a thumbnail of a combined commodity main image and combined commodity selection cost information, and the to-be-combined commodity selection trigger control at least displays a thumbnail of a to-be-combined commodity main image; therefore, style information and purchase cost information of selectable combined commodities can be conveniently checked on the to-be-combined commodity detail page, style information of selectable to-be-combined commodities can be checked at the same time, and a user can conveniently decide whether to select the combined commodities or not based on a combined commodity selection trigger control and a to-be-combined commodity selection trigger control. And thus, the purchasing efficiency of the multiple commodities is improved.

The invention discloses a differential bidding method and system based on dynamic order aggregation, and the method comprises the steps: carrying out the multi-channel aggregation of orders, constructing an order pool group, carrying out the differential bidding (transfer bidding / reverse auction), carrying out the replacement distribution, avoiding the loss of orders, and fusing an anti-communication mechanism and cross-platform credit evaluation to guarantee the fairness and safety. The system carries out fund trusteeship through an intelligent contract, and transaction fund safety is ensured. The method is suitable for multiple scenes such as B2B, B2C, O2O, presale, customization and the like, the purchase cost, the marketing cost and the unsalable risk can be effectively reduced, and the method has remarkable economic benefits and practical values.

The invention discloses an aluminum plate strip foil processing enterprise secondary aluminum resource use profit accurate accounting method, and the method specifically comprises the following steps: S1, a single-batch secondary aluminum profit accounting method is used for calculating the independent profit generated in the use process of secondary aluminum of a specific purchase batch; s2, a monthly secondary aluminum profit accounting method used for calculating the total profit generated by monthly secondary aluminum use of an enterprise; and S3, a cross-period inventory cost rolling mechanism which is used for solving the inventory cost accounting problem during cross-month use of the secondary aluminum purchase batch and ensuring that the historical purchase cost continuously affects subsequent monthly profit calculation. The beneficial effects of the invention are that: accurate traceability: profit independent accounting of secondary aluminum of a specific purchase batch is realized; dynamic response: the influence of market fluctuation on profit is reflected in real time through an SMM price linkage mechanism; cost continuity: inventory cost rolling calculation eliminates inter-period profit distortion; and decision support: quantifying economic benefits of different secondary aluminum suppliers / purchase time points, and guiding purchase strategy optimization.

The present application relates to the technical field of power system resource optimization scheduling, and particularly relates to a deep scheduling method and system for deep peak regulation of coal-fired units, which comprises the following steps: collecting multi-dimensional operation parameters of the unit, deep peak regulation instructions and carbon price signals of the carbon emission trading market, and realizing timestamp alignment; constructing a five-dimensional safety constraint model associated with the economic cost boundary; taking the maximization of peak regulation income as the target, constructing an objective function by fusing the coal purchase cost, carbon trading cost and equipment maintenance cost, solving the economic scheduling scheme by using a dynamic programming algorithm, and executing a cross-system collaborative strategy; based on the LSTM, predicting the equipment life and digital twin simulation, and triggering the cross-system correction strategy. The present application solves the business collaboration problem of economy, environmental protection and equipment reliability in deep peak regulation, and realizes the maximization of peak regulation income of coal-fired units under the power market environment.

The invention discloses an enterprise internal acquisition cost optimization decision support system based on big data analysis. The system comprises a data aggregation and integration module, a purchase behavior feature extraction module, a cost driving factor deconstruction module, a supplier multi-dimensional evaluation module, a purchase strategy intelligent customization module, an enterprise purchase data warehouse and an enterprise purchase supervision terminal. The purchase strategy suitable for the enterprise is intelligently generated by integrating the purchase behavior characteristics, the cost driving factors and the supplier evaluation result, the purchase cost composition and the dynamic change can be accurately informed, scientific and reasonable purchase decision suggestions are provided for the enterprise, the purchase cost is effectively reduced, and the purchase efficiency is improved. And the execution effect of the purchasing strategy is monitored, evaluated and fed back in real time, so that each link of the purchasing process can be monitored in real time, potential problems can be found and solved in time, the purchasing efficiency and quality are improved, the optimization of the purchasing cost and the maximization of the overall benefit are realized, the purchasing supervision difficulty of an enterprise is remarkably reduced, and the intelligent level is high.

The present invention discloses a pricing method and system for cascade hydropower interprovincial power transmission that responds to the peak-shaving demand of the receiving power grid, and belongs to the field of hydropower generation. First, the peak, flat and valley periods of electricity prices in the receiving provinces are divided, and a feasible set of time-of-use electricity price schemes is formulated; then a cascade hydropower multi-province power generation model is established; finally, the time-of-use electricity price scheme is input into the cascade hydropower multi-province power generation model, and after calculating the 24-hour external transmission output curve of the cascade hydropower, two evaluation indicators, namely the power purchase cost and the peak-shaving effect of the receiving power grid, are calculated. The time-of-use electricity price scheme is optimized through a non-dominated sorting algorithm, and the indicator weights are set and a preference function is defined. The scheme with the smallest preference function value is selected as the optimal time-of-use pricing scheme. Under the condition of meeting the maximum power generation income, the present invention effectively promotes its response to the peak-shaving demand of the receiving power grid, realizes the balance of spontaneous power generation between the sending end and the receiving end at the hourly level, and the power purchase cost of the receiving power grid is controllable, thereby achieving a win-win situation for the power plant and the receiving power grid.

The invention discloses an optimal distribution method and system based on contract electric quantity and carbon quota, and belongs to the technical field of electric power. The method comprises a multi-time-scale production benefit model and an environmental benefit model. Data preprocessing: determining parameters of the model, including electricity price, renewable energy generating capacity, thermal power generation capacity, contract electric quantity, carbon market and other related parameters; based on a multi-time scale production benefit model, a monthly, daily and real-time user production benefit model and a user electricity purchase cost model are respectively established; based on a multi-time-scale environmental benefit model, monthly and daily user environmental benefit models are respectively established, and a free carbon quota optimal distribution mode is adopted; the model can help a high-energy-consumption user to formulate an optimal contract electric quantity and carbon quota decomposition strategy, the energy consumption cost is reduced, and the economic benefit is improved.

The invention relates to a closed-loop purchase management method and system based on secondary bidding and dynamic evaluation, and belongs to the technical field of purchase management. At least three suppliers are screened out from candidate suppliers through competitive negotiation, and value-added service commitments of the suppliers are locked; during actual purchase, the entered supplier performs real-time secondary bidding, and the purchaser selects the optimal price based on the quoted price and the service; afterwards, quantitative ranking is carried out on the performance of the suppliers through a full-period dynamic evaluation system, and last position elimination and automatic position complementing are carried out according to the ranking. According to the invention, through constructing a closed-loop management system of negotiation, secondary bidding, dynamic evaluation and elimination and space complement, refined purchase of price real-time change objects such as air tickets and hotels is realized. The method can effectively stimulate the continuous competition of suppliers, significantly reduce the purchase cost and supporting expenditure, transfer the risks of withdrawing, changing, pick-up and the like to the suppliers, comprehensively improve the service response speed and quality, and enhance the purchase flexibility and the supply chain management efficiency.

The present invention discloses a method for constructing a time-of-use electricity price model considering energy suppliers and energy consumers, including: obtaining basic parameters; establishing an optimized time-of-use electricity price model considering energy suppliers and energy consumers through the Stackelberg game theory, the model including a first model and a second model, the first model including a first objective function and a first constraint condition, and the second model including a second objective function and a second constraint condition; substituting the basic parameters into the first model, solving the first model with the goal of maximizing the profit of the energy supplier, and outputting a time-of-use electricity price plan; substituting the basic parameters into the second model, solving the second model with the goal of minimizing the electricity purchase cost of the energy consumer, and outputting an energy demand plan. It can be seen that the present invention fully considers the interaction between energy suppliers and energy consumers, and realizes the maximization of the profit of energy suppliers and the minimization of the cost of energy consumers.

The invention relates to the technical field of metal price prediction, in particular to a precious metal abrupt change point prediction method, which comprises the following steps: identifying a time sequence abrupt change point according to a CUSUM control chart method, and introducing acceptance degree parameters representing investors on precious metal price small fluctuation to construct a price coarse graining method. Financial behaviors of investors are analyzed according to evolution of global topological indexes (betweenness) at the mutation points, optimal parameters of the complex network are determined, change characteristics of mutation point modals in the complex network are obtained, precious metal price modals are predicted according to an LSTM prediction method, the occurrence time of the next mutation point is determined, and the optimal parameters of the complex network are obtained. According to the invention, the research of the price abrupt change point is integrated into the complex network model of the precious metal price modal prediction, the accuracy of the price modal prediction is improved, and a manager can obtain the criticality of the price abrupt change point more accurately, so that the supply chain, the inventory and the purchase plan are adjusted in time, and the purchase cost is reduced.

The present invention discloses a main-chain and side-chain dual-chain interactive power transaction interaction method, device and storage medium, the method comprising: obtaining transaction declaration information of new energy power plants and power users and uploading it to the blockchain main chain; according to the transaction declaration information, with the goal of minimizing the power purchase cost of power users and maximizing the revenue of new energy power plants, calculating the market clearing electricity price and clearing electricity quantity for each time period a day ago and synchronizing them to the side chain; obtaining the power demand of power users in each time period of the day and subtracting the clearing electricity quantity of the corresponding time period a day ago, to obtain the power deviation of the corresponding time period; according to the power deviation of each time period and a preset deviation assessment threshold, determining the buyer user and seller user participating in the deviation transaction in the corresponding time period of the day and the quotation strategy; according to the buyer user and the seller user and the quotation strategy, with the goal of minimizing the cost of absorbing power deviation, calculating the deviation transaction result and synchronizing it to the main chain; the present invention can improve the efficiency and security of transactions.

This application relates to a pricing method and apparatus for charging and discharging at electric vehicle charging stations within industrial and commercial parks. The method includes: determining the charging and discharging energy cost and battery aging cost for each vehicle model within the target park; constructing a decision model for the owner's charging and discharging power curve for each target electric vehicle under each vehicle model based on charging and discharging price information, charging and discharging energy cost, and battery aging cost; determining the electricity market purchase cost, owner electricity interaction cost, and the number of target electric vehicles corresponding to each vehicle model within the target park; and constructing and solving an optimization decision model for the target park based on the owner's charging and discharging power curve decision model to obtain the target charging and discharging price information for the target park. This solves the problems of existing charging station pricing methods that do not use game theory to model the conflict of interest among parties, and that are unable to effectively consider battery aging costs, thus failing to provide reasonable pricing for electric vehicle services.

The invention discloses a double-layer optimization method and system for a new storage complex to participate in a two-stage electricity market, and relates to the technical field of electricity transaction, and the method comprises the steps: obtaining the prediction data of wind power and photovoltaic power, market data, and the state of a hydropower unit in an inter-province and intra-province two-stage electricity market; constructing a lower-layer optimization model with the net income maximization of the new storage complex as a target, constructing an upper-layer optimization model with the minimization of the power purchase cost and the CVaR risk cost as a target, converting and combining the lower-layer optimization model to the upper-layer optimization model to form a single-layer MILP model, and outputting an optimal scheduling plan; identifying the potential execution deviation of the medium and long term contract based on the prediction data before operation, and executing a corresponding contract adjustment strategy; and after the settlement period is finished, reconstructing a profit distribution scene based on the actual operation data, and obtaining the income of each member. According to the invention, a risk management mechanism is introduced, so that an operation strategy can effectively avoid a huge loss risk caused by extreme electricity price fluctuation while pursuing high income.

The utility model discloses a transfer jacking and transplanting device for AGV conveying and robot grabbing, and relates to the technical field of AGV conveying and robot grabbing transfer, the transfer jacking and transplanting device comprises a base and a jacking mechanism, and the jacking mechanism is fixed in the middle of the base. According to the design, the tool jacking function is achieved, the position needed for grabbing the bottom of the tool is reserved, the optimal effect of automatic grabbing and placing is achieved, the purchase cost of accessories is reduced, the functions of all the accessories are reasonably applied, and therefore low-purchase-cost multifunctional cleaning application is achieved, the occupied space of an installation site is reduced, and the installation efficiency is improved. The corresponding personnel investment cost is reduced, the transportation / production / purchase / installation cost is reduced, the utilization rate of the cleaning device is increased, the problems that AGV batch tools are conveyed to a transfer transplanting device, and grabbing devices are used for taking and placing in batches are solved, the supply problem caused by the low AGV conveying efficiency is solved, and through program standardization, the production efficiency is improved. Program debugging is reduced, and multi-machine unified mutual test can be achieved.