53 results about "Electricity pricing" patented technology

The invention relates to the technical field of artificial intelligence, and provides a thermal power generating unit operation optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source input data which at least comprises thermal power generating unit operation data and power market data; performing coal consumption prediction based on the operation data of the thermal power generating unit to obtain a first prediction parameter representing the coal consumption; determining a second prediction parameter representing the power price signal based on the power market data and the first prediction parameter; based on the first prediction parameter, the second prediction parameter and the multi-source input data, output and declaration parameters used for guiding the thermal power generating unit to optimize the operation state are generated. According to the method, the technical path of multi-source data fusion and coupling of internal coal consumption and external price signals for step-by-step prediction and comprehensive optimization is adopted, accurate prediction and dynamic optimization of the operation parameters of the thermal power generating unit are achieved, and the method has the advantages of being high in prediction precision and flexible in operation control.

This application relates to a control method, apparatus, and television device. The method includes: obtaining the current electricity price period based on peak-valley tiered electricity price data for the user's region, and obtaining the user's household electricity consumption data; determining whether preset energy-saving conditions are met based on the current electricity price period and household electricity consumption data; when the preset energy-saving conditions are met, generating an adjustment strategy based on the preset energy-saving conditions and the television device's equipment parameter data; and controlling the television device to execute the adjustment strategy, so that the television device can reduce power consumption while maintaining continuous playback of media content. Using this application, intelligent energy-saving control linked to peak-valley tiered electricity pricing can be achieved, balancing energy saving of the television device with the viewing experience.

The invention discloses a central cold and hot water intelligent collaborative centralized control energy efficiency management system which comprises a central cold and hot water system, a central air conditioning system, an AI cloud energy efficiency cloud platform, a central CPU processor, a 5G communication module, an efficient host, an intelligent human-computer interface, a cold water output permanent magnet variable frequency water pump and a hot water output permanent magnet variable frequency water pump. The central cold and hot water system comprises a cold water heat preservation water tank and a hot water heat preservation water tank, the cold water heat preservation water tank is provided with a cold water unit and a cold water output pipeline, and the hot water heat preservation water tank is provided with a hot water unit and a hot water output pipeline. The efficient host is used for executing the electricity price strategy, responding to electricity price fluctuation and controlling the water chilling unit and the hot water unit. The intelligent cooperative centralized control energy efficiency management can be realized, the output power of the cold and hot water sources can be reversely adjusted based on the tail end load prediction, the cold and hot source output is matched with the tail end demand, the peak-valley arbitrage can be realized by combining the electricity price prediction curve and the temperature energy storage technology, and the operation cost is reduced.

The invention discloses an electric vehicle distributed charging optimization method and system based on a carbon emission sensitive dynamic electricity price, and the method comprises the steps: setting an initial iteration strategy based on a controllable electric vehicle load model, and averagely distributing the needed charging amount to all charging time periods; defining the size of an algorithm convergence criterion according to the required convergence precision and the number of the electric vehicles; constructing a dynamic electricity price containing coupling of a load item and a carbon emission item, and calculating an EVn cost function according to the dynamic electricity price; starting a kth round of iteration, traversing all EVn in each round of iteration, and counting k from 0; other electric vehicle charging power vectors except the EVn are calculated; after each iteration, checking whether a convergence criterion is met or not; if yes, the obtained load curve is an approximate solution of Nash equilibrium, and if not, the charging strategy is updated repeatedly; and after the load curve under the dynamic electricity price mechanism is obtained, the load curve under the time-sharing static electricity price with the same electricity price level is obtained through calculation. The method is suitable for electric vehicle group charging management in a community scene.

An automatic control method, system, equipment, and medium for a series refiner unit are disclosed, relating to the field of automatic control. The method includes the following steps: acquiring pulp demand information; configuring the operating mode of the series refiner unit based on the pulp demand information; determining the operating parameters of the pulping process based on the refiner unit's operating mode and acquiring time-of-use electricity price information, the operating parameters including at least the energy consumption per unit time of the pulper and the energy consumption per unit time of the refiner unit; establishing an operating energy consumption model for the pulping process based on the operating parameters and time-of-use electricity price information; optimizing the operating energy consumption model to minimize energy consumption; and planning the start-up and shutdown states of the series refiner unit and pulper in subsequent operating time units of the pulping process based on the optimization results. By adopting the technical solution provided in this application, the equipment involved in the pulping process can be better controlled, thereby utilizing the time-of-use electricity pricing system to reduce the total energy cost of the pulping process.

The invention belongs to the technical field of energy optimization scheduling, and provides a comprehensive energy planning method and system considering the regulation capacity of a regulation and storage pool of a sewage plant, and the method comprises the steps: designing an economic cost function of a comprehensive energy system; the economic cost of the comprehensive energy system comprises investment cost, cost of purchasing electricity from a power distribution network, cost of purchasing natural gas and operation and maintenance cost of the comprehensive energy system; based on an integrated energy system economic cost function, establishing an integrated energy planning model; and solving the integrated energy planning model to obtain a design scheme of the integrated energy system. Under the condition of considering capacity limitation of the regulation and storage pool and natural gas and electric power price, the starting time and power of the water pump of the regulation and storage pool are optimized, and the water pump is started to pump the to-be-treated sewage into the regulation and storage pool as far as possible in the power margin period of the power distribution network, so that optimal configuration of energy of a sewage plant and reduction of energy consumption cost are realized.

A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply / demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.

The application discloses an extreme value dynamic correction day-ahead electricity price probability prediction method for tail risk, and the method comprises the following steps: an electricity price extreme value extraction method based on a quantized sliding window algorithm is used to extract candidate extreme value points from an electricity price time sequence, and target extreme value points corresponding to sharp peak electricity prices and high plateau electricity prices are screened; a generalized extreme value distribution is used for statistical modeling of the target extreme value points to determine generalized extreme value distribution parameters, which are used for describing tail risk; a deep learning model is constructed, Box-Cox transformation and Huber loss function are optimized through a joint training mode, a predicted mean value and a predicted standard deviation of the electricity price are outputted, and a predicted interval of day-ahead electricity price is constructed; a dynamic calibration mechanism is introduced, the predicted mean value is corrected and the predicted interval is dynamically adjusted based on the generalized extreme value distribution parameters, so that the reliability of the predicted interval when an extreme price event occurs is improved. The application significantly improves the accuracy and reliability of extreme fluctuation prediction of the electricity price.

This invention relates to the field of electric vehicle charging and discharging technology, and more particularly to a method, system, and computer-readable storage medium for extrapolating a vehicle-to-grid (V2G) interaction supply-demand coordination model. The method first collects data and determines model input parameters based on local policies, electricity pricing mechanisms, and technological development trends. Then, it constructs a user utility function, eliminates differences in indicator dimensions through normalization, and calculates the probability of user selection for each charging mode, thus constructing a demand-side mode selection model. Based on user selection probabilities and facility operation indicators, it establishes a model for calculating facility expansion scale, and uses a dual-objective optimization framework to balance maximizing user satisfaction with minimizing investment costs to determine the annual number of new facilities. The number of new facilities is fed back to the user model to update facility accessibility parameters until the supply and demand status meets preset convergence conditions. This invention significantly improves the scientific nature and prediction accuracy of V2G interaction planning through dynamic closed-loop modeling of supply and demand coordination and a dual-objective optimization strategy.

The invention discloses a new energy distribution and storage control system and method based on artificial intelligence, and belongs to the technical field of new energy distribution and storage control. The method comprises the following steps; acquiring power consumption demand and environment data and structure data of the new energy power station; wherein the electricity demand quantity is an electricity power time change curve within a preset time; inputting the environment data and the structure data into a constructed new energy power generation prediction model to obtain a power generation time change curve of the new energy power station within a preset time; constructing an electric power price prediction model to predict the electric power price, and obtaining an electric power price time change sequence within a preset time; inputting the power generation time change curve, the power price time change curve and the power consumption demand into a constructed energy storage system control model to obtain an energy storage control strategy in a preset time; and controlling the energy storage system to perform charging and discharging operation according to the energy storage control strategy.

The invention discloses a day-ahead electricity price prediction method based on market subject behavior pattern and finite rationality theory simulation, and belongs to the technical field of electricity price prediction, and the method comprises the steps: S1, analyzing the day-ahead market transaction process of typical markets at home and abroad; s2, introducing a simulation declaration information strategy coefficient, constructing a day-ahead power price prediction framework based on a market participant behavior mode and finite rationality theory simulation, and representing and simulating market subject declaration information; s3, predicting market subject declaration information based on a typical time sequence prediction model; and S4, combining the predicted declaration information of each subject, superposing to form the overall declaration quantity-price curve information of the market, and obtaining the predicted day-ahead electricity price. According to the method, the problems that the influence factor dynamic characteristics of the day-ahead electricity price change are difficult to extract and the forming mechanism of the day-ahead electricity price is difficult to reflect in the prior art are solved. According to the method, more accurate day-ahead electricity price prediction is obtained by reasonably simulating the coupling relationship between the market declaration behavior information and the day-ahead electricity price.

This application relates to a method, apparatus, and electronic equipment for assessing the demand response potential of a zero-carbon hydrogen metallurgical system. The method includes: constructing an operating model of a hydrogen-based shaft furnace considering the metallization rate of direct reduced iron; establishing an objective function based on the production baseline load of the zero-carbon hydrogen metallurgical system; constructing a demand response potential assessment model for the zero-carbon hydrogen metallurgical system based on the objective function, constraints of the electrolyzer in the zero-carbon hydrogen metallurgical system, and constraints of the operating model of the hydrogen-based shaft furnace; determining the demand response potential assessment index for the zero-carbon hydrogen metallurgical system; and assessing the demand response potential of the zero-carbon hydrogen metallurgical system under real-time electricity pricing based on the assessment model and the assessment index. This solves the problems in related technologies, such as the lack of assessment of the flexibility of the zero-carbon hydrogen metallurgical system and the limited utilization of flexibility resources, leading to severe wind and solar power curtailment, grid backflow, and voltage exceedances, resulting in high production costs.

The present application belongs to the field of user-side configuration energy storage system, and discloses a user-side energy storage configuration scale optimization method and related device, by obtaining the load curve data and power price data of the user in the preset historical period, including the peak, peak, flat period and valley period price, constructing the economic benefit model with the discharge income and charging cost difference of the energy storage system in the preset time as the objective function, setting the constraint conditions including the charge and discharge power limit, the total power limit after the user load superposition and the real-time capacity change relationship, iteratively solving with the maximum objective function as the optimization target, and obtaining the optimal energy storage configuration power and scale. The method significantly improves the matching degree of the energy storage configuration scale and the real load curve of the user, effectively avoids the discharge shortage in the peak period and the capacity waste in the valley period, optimizes the energy storage system operation efficiency, enhances the economic benefit evaluation accuracy, thereby meeting the actual needs of energy storage project investment and configuration optimization.

The invention relates to the technical field of energy consumption management and production scheduling in the dairy product production process, in particular to an energy consumption optimization method and system for dairy product filling equipment. The method comprises the steps that dairy product filling production plans, variety and specification information and multi-energy historical and real-time energy consumption data are obtained, historical energy consumption is cleaned, a calibration period is divided in combination with time-of-use electricity price, and unit product baseline energy consumption is calculated according to varieties; calculating the total energy consumption of electric energy, steam and compressed air in each time period based on a real-time multi-energy metering result to obtain the actual energy consumption and relative baseline deviation of a unit product in a statistical period of each variety; a comprehensive optimization objective function is constructed according to time-of-use electricity price, unit product energy consumption deviation and filling rhythm deviation, filling production scheduling and rhythm parameters are optimized under productivity, process and safety constraints, a production task allocation and equipment energy-saving control instruction is generated, rolling updating is combined with an operation result, and an energy-saving control function is established. And dairy product filling multi-energy collaborative energy saving and unit product energy consumption refined management and control are achieved.

The application discloses a kind of short process steel enterprise participates in the peak shaving control method of high proportion renewable energy system.The short process steel enterprise participates in the peak shaving control method of high proportion renewable energy system of the application includes: design fusion short process steel enterprise's renewable energy peak shaving architecture and build short process steel enterprise load response model;Establish the non-cooperative game process based on grid demand release, enterprise response solution and aggregator information interaction;With the peak shaving demand in high proportion renewable energy power system and enterprise production electricity pricing and incentive double signal response mechanism are formulated;Build source and load double-layer economic dispatching model, hierarchical iteration solution and reach source and load benefit balance;The effectiveness of the peak shaving control method provided in the application is analyzed.The application aims to comprehensively consider the game equilibrium of grid peak shaving demand and short process steel production electricity, to improve the renewable energy consumption rate of power grid and thus improve the grid peak shaving effect, with the advantages of scientific and reasonable, strong applicability, good effect.

This invention belongs to the field of power system technology, specifically relating to a method and system for mining rules for electricity price and fee consistency verification. The method includes: acquiring a verification dataset and calculating the equivalence partitions of quantity, price, and fee data; using a depth-first spatial search strategy to mine data consistency relationships, obtaining a set of consistency relationships in the data; using the obtained set of consistency relationships to perform rule parsing on the quantity, price, and fee consistency relationships; and identifying rare data in the quantity, price, and fee data through rule parsing. This invention applies a novel consistency rule mining method for categorized data to the verification of electricity price and fee data. It can proactively mine potential rules in the data, effectively overcoming the shortcomings of previous manual rule definition methods, such as high workload, long time consumption, insufficient rule coverage, and the ability to only discover abnormal business data covered by defined rules. This assists business personnel in completing the verification of electricity marketing quantity, price, and fee data more intelligently and comprehensively.

A collaborative scheduling method for virtual power plants and electrolytic aluminum enterprises based on dynamic electricity pricing and a two-layer optimization approach is proposed. This method includes: constructing a two-layer optimization scheduling framework between virtual power plants and electrolytic aluminum enterprises, where the upper layer is the virtual power plant scheduling model and the lower layer is the electrolytic aluminum enterprise response model; using an LSTM neural network to perform day-ahead rolling forecasts of wind and solar power output, and using the forecast results as scheduling input; establishing a multi-energy coupled operation model within the virtual power plant and considering carbon trading costs; designing an iterative solution mechanism based on dynamic electricity pricing, guiding electrolytic aluminum enterprises to adjust their load through price signals to achieve source-load collaborative optimization; and setting a convergence criterion, terminating the iteration when both the change in total cost of the virtual power plant and the change in total revenue of the electrolytic aluminum enterprise are below a set threshold, outputting the optimal electricity price and scheduling plan. This method improves the absorption level of renewable energy sources such as wind and solar power, effectively reduces the overall system operating cost, and significantly enhances the peak-shaving capacity and operational flexibility of the power system.

The invention provides a flow shop scheduling dual-objective optimization method based on a bacterial foraging optimization algorithm, and relates to the technical field of production and manufacturing management. The method specifically comprises the steps of obtaining original data of production scheduling in an industrial assembly line, and constructing a coupling coding scheme based on a task sequence and a delay period matrix under the background of time-of-use electricity price; an initial population is generated by adopting a hybrid heuristic initialization strategy, wherein each individual represents a complete scheduling scheme through a coupling coding strategy based on a task sequence and a delay matrix. According to the method, global optimization is carried out on a solution space based on an improved multi-target bacterial foraging optimization algorithm, and finally, an optimal individual is output and serves as an optimal scheduling scheme of a flow shop. According to the method, the problem of double-target optimization faced by original equipment manufacturers in flow shop scheduling under a time-of-use electricity price strategy is effectively solved, and meanwhile, the production efficiency is improved and the electric charge expenditure is reduced.

The invention discloses an energy storage rental energy system master-slave game optimization method suitable for a wind power fluctuation scene, and belongs to the technical field of power grid dispatching. The method comprises the following steps: S1, dividing an energy system into an upper-layer power distribution network operator, a middle-layer pumped storage operator and a lower-layer microgrid group alliance, and constructing a three-layer master-slave game optimization model; and S2, solving the master-slave game optimization model to obtain an equilibrium solution, and scheduling the energy system based on the equilibrium solution. According to the method, a three-layer master-slave game model of a power distribution network operator-pumped storage operator-microgrid group alliance is constructed, and the time-sharing purchase and sale electricity price and the energy storage lease price are used as unified price signals, so that collaborative decision and benefit coordination among multiple subjects are realized.

This invention provides a method for storing and releasing cold energy in refrigerated trucks using peak-valley electricity pricing based on modular phase change cold storage. By coordinating off-peak electricity storage with a cold core module, it significantly reduces the operating costs of cold chain logistics and enables peak-valley electricity price arbitrage. The cold core module is charged during off-peak hours and replaces or assists mechanical refrigeration during peak hours. It can also serve as a mobile cold source to supplement cold storage, improving cold energy utilization efficiency. The system uses high-performance phase change materials and optimized heat exchange structures to effectively ensure low-temperature stability during transportation and prevent chain breakage risks. The cold core module supports flexible deployment between refrigerated trucks and cold storage facilities, enabling dynamic allocation of cold energy in warehousing and transportation scenarios. The modular quick-installation design facilitates rapid replacement and maintenance under high-frequency transportation, and, combined with an intelligent dual-modal temperature control system, ensures a constant temperature inside the truck. The materials exhibit good cycle stability, long lifespan, and no leakage risk. The overall solution demonstrates outstanding energy-saving performance, significantly reduces carbon emissions, and combines economic, environmental, and scalable advantages.

This invention discloses a peak-valley electricity pricing intelligent equipment control system based on multimodal video analysis, including a video acquisition module, an electricity pricing strategy acquisition module, an AI multimodal analysis engine, a dynamic scheduling controller, and an economic analysis module. The advantages of this invention compared to existing technologies are: Instead of focusing solely on energy costs in traditional equipment control, this invention introduces equipment lifespan and carbon emissions as optimization objectives, constructing a multi-objective optimization model to achieve synergistic optimization of economic and environmental benefits, aligning with the global trend of low-carbon development. Through this innovation, in industrial applications, the total lifespan cost of equipment can be reduced by 15%-20%, and carbon emissions reduced by 10%-15%.

This application proposes a production scheduling collaborative optimization method considering time-of-use electricity pricing and AGV transportation, relating to the field of workshop scheduling technology. The method includes: reverse-order traversal of the process sequence corresponding to the target scheme individual; determining the optimal displacement of the target process within the maximum feasible right shift range corresponding to the currently traversed target process; decoding the target scheme individual at the optimal displacement and initial position based on a non-dominated sorting genetic algorithm to obtain a first bi-objective value and a second bi-objective value; determining the total electricity cost savings and maximum delay increment based on the difference between the first and second bi-objective values; and using an investment return strategy to determine whether the target process should be right-shifted according to the optimal displacement; updating the bi-objective values ​​of the target scheme individual based on the determination result, thereby completing the update of the entire external archive set. This application can provide a series of optimal production plans that achieve a balance between electricity cost reduction and on-time delivery.

The invention relates to the field of electric power and computer application, and provides an automatic processing method for electric charge error data. The method comprises the following steps: acquiring reference data from a marketing management system; electric charge error event data are received, and the electric charge error event data comprise a user identifier, an error time period and an error electric quantity; based on the user identifier, matching a corresponding electricity price strategy from the reference data, and loading preset dynamic calculation logic corresponding to the user category; in response to a configuration operation on the control point, activating or disabling a corresponding calculation module, and carrying out parallel or serial processing on the error electric quantity by utilizing the activated calculation module to generate refund and supplement expense details decomposed according to the electric charge constituent elements; and outputting the fee return and supplement details decomposed according to the electric charge constituent elements for comparison with the original billing result of the system. According to the invention, through structured data processing and configurable calculation logic, accurate and efficient automatic processing of electric charge refund and supplement is realized.

The invention discloses a flexible electricity utilization pricing and demand response method adaptive to high-permeability new energy. The method comprises the following steps: firstly, constructing a two-stage Stackelberg game model comprising an aggregator and a plurality of flexible electricity utilization users; the aggregator is used as a leader, and a dynamic electricity price mechanism which associates the real-time electricity price with the new energy output and the total declared electricity quantity of the user is published in the day-ahead stage. Each user is used as a follower, and the day-ahead declaration electric quantity and the real-time electric quantity of the user are independently decided according to the electricity price mechanism to maximize the income of the user, so that the non-cooperative game of the user side is formed and the balance is solved. Afterwards, the aggregator takes the balance result of the user side as feedback, the comprehensive income of the aggregator is maximized by optimizing parameters in an electricity price mechanism, and finally balance of the whole Stackelberg game is formed. And in the real-time operation stage, the aggregator schedules the controllable power supply according to the equalization result and the actual output of the new energy, and preferentially consumes the new energy.

A method and system for constructing a time-series attention model for electricity prices that integrates grid congestion information is disclosed. The method includes: collecting grid state, market state, and electricity price data, and constructing a dataset using window partitioning; building a grid congestion detection and vector fusion module to determine grid congestion status and generate congestion state feature vectors, fusing these feature vectors with grid state vectors and market state vectors to form exogenous variables; constructing a time-series attention network to perform feature mapping on exogenous variables, embedding vectors into endogenous and exogenous variables, and obtaining electricity price prediction results after fusion using a dual attention mechanism; training and validating the grid congestion detection and vector fusion module and the time-series attention network; and testing the grid congestion detection and vector fusion module and the time-series attention network validated in step 4 using a test set. This invention can improve the accuracy and stability of electricity price prediction under complex operating scenarios.

A bidirectional real-time price prediction method for demand response comprises (a) acquiring user-related information and real-time electricity price data for a smart home user; (b) generating future electricity price prediction information by applying the user-related information and the real-time electricity price to a deep learning-based prediction model; (c) generating power consumption scheduling results for smart home devices based on the electricity price prediction information; (d) analyzing the power consumption scheduling results to calculate the hourly shifted power and the flexible appliance consumption ratio, and calculating a mobility adjustment value and a consumption adjustment value based on the hourly shifted power and the flexible appliance consumption ratio, respectively; (e) calculating an incentive-penalty weight value according to the power consumption pattern using the mobility adjustment value and the consumption adjustment value; and (f) reflecting the incentive-penalty weight value in the electricity price prediction information to derive the bidirectional real-time electricity price.

The invention discloses a power flow optimization and electricity price mechanism-oriented vehicle-pile-network active optimization scheduling method, and relates to the technical field of electric vehicle charging scheduling, and the method comprises the steps: constructing an upper-layer multi-objective optimization function corresponding to a power grid scheduling layer through the minimum load fluctuation, the maximum charging station income and the minimum network loss penalty; constructing a lower-layer target optimization function corresponding to the user response layer at the minimum user cost; and executing double-layer iterative optimization of the upper-layer multi-objective optimization function and the lower-layer objective optimization function, and outputting dynamic market information, a charging station charging and discharging strategy and an electric vehicle charging and discharging strategy to execute optimal scheduling. The method has the beneficial effects that the balance of user cost is realized, so that the scheduling optimization strategy not only meets the charging station income and user cost requirements, but also can ensure the load balance of the power grid as much as possible, and the collaborative optimization among the power distribution network, the charging station and the user is realized.

This invention provides a predictive control method for a district cooling system based on a collaborative model spanning the entire power supply, grid, and load chain, belonging to the field of intelligent control technology for cooling systems. The method involves collecting and preprocessing meteorological and operational characteristic data of the system; inputting the data into an Attention-LSTM cooling load prediction model, determining the optimal step size using thermal inertia analysis, and outputting a cooling load prediction sequence; constructing a quasi-steady-state hydraulic-thermal coupling mechanism model of the pipe network based on unified energy path theory; establishing an optimization model with three objectives, including pump power consumption, using load prediction as input and the coupling model as constraints; employing a multi-objective particle swarm optimization algorithm to obtain the optimal operating parameters that satisfy the thermal imbalance constraint; and combining peak-valley electricity pricing to formulate a collaborative scheduling strategy for chiller units and cold storage devices, generating the final control command. This method effectively solves the problems of insufficient terminal cooling supply and localized overcooling caused by hydraulic imbalance and large hysteresis characteristics in the pipe network, achieving optimal control of operating costs.

This disclosure relates to an information processing apparatus. A market electricity price forecasting unit forecasts the transaction price of electricity traded in a peer-to-peer (P2P) electricity trading market. The transaction price includes the price of renewable energy electricity and the price of non-renewable energy electricity. A renewable energy index setting unit sets a renewable energy index indicating the extent of renewable energy electricity in the electricity purchased in the P2P electricity trading market. A transaction price setting unit sets the expected transaction price in an electricity trading plan based on the forecasted transaction price of the electricity and the renewable energy index. A trading plan creation unit creates the electricity trading plan based on the expected transaction price.