58 results about "Electricity market" patented technology

This invention discloses a modeling method based on a multi-product electricity market clearing and settlement mechanism. The method includes identifying key concepts in the electricity market, including homogeneous products, heterogeneous products, product groups, and product conversion; determining data based on a bidding function; determining data based on a true function; constructing a general model for centralized bidding market bidding and settlement; and constructing a general model for bidding market clearing. The mathematical model established by this invention can better express the relationships between various factors in the electricity market.

A method and system for multi-market collaborative optimization of independent energy storage in the electricity market is proposed. This method acquires the statistical distribution characteristics, peak-valley structure characteristics, and extreme event characteristics of daily historical electricity price sequences, and clusters them to obtain typical days. Each typical day is divided into normal days and risk days. At the bottom layer, based on the operational data of each energy storage unit on risk days, the constraints of intraday decision parameters are adjusted to meet the constraints of safe energy storage operation. At the top layer, using the operational data of each energy storage unit on normal days, and aiming at the optimal market economic objective, the baseline values ​​of intraday decision parameters for each energy storage unit are determined under the constraints of the adjusted intraday decision parameters. At the bottom layer, based on the operational data of each energy storage unit on normal days and the baseline values ​​of intraday decision parameters, the baseline values ​​of intraday decision parameters for each energy storage unit are optimized to maximize energy storage operating efficiency and minimize energy loss, thereby obtaining a profit-maximizing control strategy under multi-market collaboration to achieve executable monthly and annual scheduling for the project.

A method and system for multi-market collaborative optimization of independent energy storage in the electricity market is proposed. This method acquires the statistical distribution characteristics, peak-valley structure characteristics, and extreme event characteristics of daily historical electricity price sequences, and clusters them to obtain typical days. Each typical day is divided into normal days and risk days. At the bottom layer, based on the operational data of each energy storage unit on risk days, the constraints of intraday decision parameters are adjusted to meet the constraints of safe energy storage operation. At the top layer, using the operational data of each energy storage unit on normal days, and aiming at the optimal market economic objective, the baseline values ​​of intraday decision parameters for each energy storage unit are determined under the constraints of the adjusted intraday decision parameters. At the bottom layer, based on the operational data of each energy storage unit on normal days and the baseline values ​​of intraday decision parameters, the baseline values ​​of intraday decision parameters for each energy storage unit are optimized to maximize energy storage operating efficiency and minimize energy loss, thereby obtaining a profit-maximizing control strategy under multi-market collaboration to achieve executable monthly and annual scheduling for the project.

This invention discloses a distributed bidding method for virtual power plants based on the MADDPG algorithm, belonging to the field of electricity market trading and intelligent optimization technology. The method includes the following steps: Step 1) Virtual power plant system modeling and environment construction: constructing a virtual power plant resource characteristic model; Step 2) Distributed bidding model construction: considering P2P transactions between multiple virtual power plants, constructing a distributed bidding model for the electricity-carbon coupling market; Step 3) Reinforcement learning-based model solving: addressing the multi-agent and strongly coupled characteristics of the proposed distributed bidding model, a solution method based on the MADDPG algorithm is used to solve the model. This invention can achieve efficient collaborative decision-making in bidding problems with strong coupling among multiple agents, high dimensionality, and obvious continuous decision-making characteristics, improving the economic benefits and strategy adaptability of virtual power plants, and has good scalability and engineering application value.

This invention relates to a day-ahead-intraday optimal scheduling method for energy storage systems considering photovoltaic (PV) and energy storage participation in the electricity-frequency regulation market. Compared with existing technologies, it addresses the shortcomings of traditional PV-energy storage power plant operation modes, such as low energy storage utilization, long investment cost recovery period, lack of multi-timescale optimization considerations, and neglect of the dynamic impact of nonlinear lifetime losses on operating costs. The invention includes the following steps: generating typical PV output scenarios and operating losses; constructing a day-ahead two-stage stochastic optimal scheduling model; formulating real-time frequency regulation power allocation rules; and day-ahead-intraday multi-timescale optimal scheduling. This invention constructs a day-ahead-intraday optimization model for PV-energy storage combined systems participating in the electricity-frequency regulation market, achieving efficient allocation of energy storage resources between the electricity market and the frequency regulation market, improving the utilization rate of energy storage devices and the economic benefits of PV-energy storage combined power plants. Based on regional control deviations, different regulation intervals are divided, and a dynamic power allocation strategy for energy storage is formulated, enhancing the grid security support capability.

We provide a scheduling system and scheduling program that create an energy usage schedule that can reduce energy costs without changing the lifestyle of consumers. [Solution] The system includes an estimation task 161 that estimates the lifestyle 3 of customer P based on the amount of electricity used measured at predetermined intervals by a smart meter 2 installed at customer P's home, a pricing plan 5 in which the energy usage charges fluctuate according to the time of day in conjunction with the market price in the wholesale electricity market 4, and a scheduling task 162 that creates an electricity usage schedule based on the lifestyle 3, such that at least a portion of the time of day when customer P uses electricity is in line with the lifestyle 3 and is a time when usage charges are low.

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.

A power supplier electricity-carbon market transaction data processing method and system. The method comprises the following steps: in a medium and long term transaction stage, constructing an electricity price and carbon quota price prediction model, adjusting daily decomposition quantity based on wind and light output prediction, and constructing a transaction decision model of the medium and long term transaction stage; constructing a double-layer decision model in a spot transaction stage, including an upper-layer power supplier electricity-carbon transaction decision model, decomposing monthly contract quantity to obtain a power supplier electricity market spot transaction strategy and carbon market transaction quota quantity, and a lower-layer market clearing model; linearizing and solving the market clearing model and the power supplier decision model through KKT conditions to obtain the electricity price, carbon price and optimal output of each unit of the electricity-carbon market. The scheme improves the comprehensive income and decision scientificity of the power supplier in the double market.

This invention discloses a method, device, and software product for optimizing the revenue of a wind-power hybrid energy storage system across multiple markets. First, based on a market compensation mechanism, a multi-market revenue calculation model is constructed, clarifying the allocation mechanism of the winning capacity in each type of market and calculating the capacity activation coefficient for each type of frequency regulation service. Building upon this, a battery degradation cost penalty mechanism based on cumulative energy throughput is introduced, transforming implicit lifespan losses into explicit economic costs embedded in the optimization objective, thus achieving a trade-off between economic benefits and equipment lifespan. Furthermore, a unified mixed-integer linear programming model is used to achieve integrated collaborative optimization of day-ahead capacity bidding and real-time power allocation. Ultimately, while ensuring the physical operational safety of the system, the method significantly improves the revenue aggregation capability and life-cycle economics of the wind-power hybrid energy storage system in multi-level electricity markets.

This invention proposes a method and system for monitoring electricity trading risks based on a multi-source signal mechanism and a sparse hybrid expert model. The method includes: collecting historical day-ahead electricity market price data and raw data on influencing factors; performing data preprocessing to construct a multi-source fusion signal from price spread signals and bidding space signals; using a random forest algorithm for feature selection; constructing a set of similar day data based on the bidding space signal and incorporating it into the training set; using a sparse hybrid expert model as the model framework, with the price spread signal and bidding space signal as targets, training different prediction models; and searching for an optimal declaration curve based on the Northern Eagle optimization search algorithm by predicting the price spread signal and bidding space signal, using the short-term power prediction curve as a benchmark. This invention effectively solves the problem of insufficient model generalization ability, realizes risk monitoring in electricity spot trading, and improves the accuracy of electricity spot trading risk assessment while enhancing the stability of the power system.

This invention belongs to the field of power market optimization and dispatching technology, and discloses a method and related apparatus for optimizing medium- and long-term trading volume of wind farms. The method includes: acquiring the wind farm output prediction curve and the predicted electricity price curve; solving the wind farm power optimization model based on the wind farm output prediction curve and the predicted electricity price curve to obtain an optimized power allocation scheme for the wind farm in medium- and long-term contracts and the spot market; the wind farm power optimization model aims to maximize the average revenue of the wind farm under various scenarios; the calculation of the average revenue under various scenarios considers the levelized cost of electricity (LCOE) of energy storage charging and discharging throughout its entire lifecycle. The technical solution disclosed in this invention can be used to optimize the allocation of medium- and long-term contract power in the wind farm power market. It considers the optimization capability of wind farms in allocating their own power after the installation of energy storage, and can significantly improve the economic benefits of wind farms.

The application discloses a coal-powder storage and release mechanism-based thermal power generating unit control method and system, and relates to the technical field of electric power energy production and heat supply. According to historical power grid load data, historical electric power market transaction information and weather forecast information, a power grid load prediction result in a future time period is predicted; according to the power grid load prediction result in the future time period and current time boiler state data, pulverizing state data and external coal bunker state, a control strategy is determined; the control strategy is a coal-powder storage strategy or a release strategy; in the case that the control strategy is the coal-powder storage strategy, a pulverizing system and a powder feeding device are controlled to store coal powder in an auxiliary external powder bunker to a target storage amount; in the case that the control strategy is the release strategy, a release instruction is sent to the auxiliary external powder bunker, so that the auxiliary external powder bunker delivers coal powder to a boiler of the thermal power generating unit according to the release strategy. The method can accurately regulate and control the thermal power generating unit.

This invention discloses a method for energy storage arbitrage, adjustable equipment power consumption optimization, and peak-shaving assistance based on the power system settlement entity, belonging to the field of distributed power resource dispatching technology. This invention uses the power system settlement entity as the aggregation unit, integrating energy storage devices and adjustable devices to form an adjustable resource pool. Through multi-mode adaptive pricing, data traceability verification, energy storage status protection, continuous smooth power control, and tiered peak-shaving response strategies, it achieves coordinated operation of peak-shaving priority, price arbitrage, and power consumption structure optimization. This invention is based entirely on published objective electricity prices for execution control, without involving price prediction, and ensures revenue certainty through multi-mode guaranteed price difference calculation, while providing full life-cycle protection for energy storage batteries. This invention is applicable to the compliant aggregation and market-oriented operation of small distributed power equipment of 50kW and below, is compatible with various electricity market pricing rules, responds to grid peak-shaving needs, and improves user revenue and system operational stability.

This invention belongs to the field of medium- and long-term power generation in new energy, and discloses a medium- and long-term power forecasting method and related equipment based on ERA5 reanalysis data. This method uses multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, obtains the climatological average irradiance, and constructs an error correction model by combining it with historical measured data from the power plant. This allows for medium- and long-term power forecasting using historical data from the same period of the power plant's climatological output. By using multi-year ERA5 reanalysis data to perform climate model analysis on the area where the power plant is located, this method can accurately capture the climatological average irradiance of the area, effectively solving the forecasting problem caused by the lack of historical data for newly built or short-term operating photovoltaic power plants. Using this method is of crucial practical significance for power plants to reasonably declare medium- and long-term trading volumes in electricity transactions and reduce revenue losses caused by positive and negative volume deviations, significantly enhancing the competitiveness of power plants in the electricity market.

This invention relates to the field of energy storage system control technology, and discloses a collaborative control method and system for energy storage cooling capacity and peak-valley arbitrage. The method includes: collecting multi-dimensional operational data of the energy storage system and electricity market transaction data; generating a thermoelectric prediction tensor using a temporal convolutional network; analyzing the thermoelectric prediction tensor to calculate the cooling urgency weight of the battery cluster and constructing a heat demand Fibonacci stack; extracting the optimal cooling task and generating an arbitrage strategy skip list based on peak-valley electricity price constraints; traversing the skip list to obtain a control instruction set and driving independent heat exchangers and intelligent refrigerant boxes to perform refined temperature control operations. This invention solves the problems of lag in traditional heat dissipation control and difficulty in coordinating peak-valley electricity price arbitrage with temperature control through thermoelectric prediction, dynamic sorting of heat demand, and matching of cooling capacity supply and demand. It achieves stable battery temperature maintenance, maximized peak-valley arbitrage profits, and fully silent operation of the energy storage system.

This invention discloses a multi-timescale market bidding method and system for energy storage systems considering dynamic opportunity costs, relating to the fields of electricity market and energy storage optimization technology. Through a dynamic opportunity cost quantification model, the potential impact of current decisions on future returns is dynamically quantified as constraints. While avoiding short-sighted decisions, a multi-market time-series coupling mechanism is constructed using the capacity state transfer equation and cross-market power balance constraints. This ensures the physical feasibility and temporal consistency of the dynamic allocation of energy storage capacity across day-ahead, intraday, and real-time markets. Furthermore, a stochastic-robust hybrid optimization framework is proposed, integrating generative adversarial network scenario generation, conditional risk value, and robustness margin to unify the optimization of expected returns and extreme risks. The constraints are transformed into optimization problems using KKT conditions and the Big M method, and solved using the ADMM distributed algorithm, ensuring the model's feasibility and real-time performance in engineering practice.

This invention provides a method and system for optimizing electricity purchase plans on the retail side based on load fractal characteristics, belonging to the field of electricity market technology. This invention extracts load fractal characteristics reflecting their correlation features by performing modal decomposition on historical load and bidding data; analyzes electricity market rules to form constraint units and identifies risk-sensitive nodes that cause fluctuations in electricity purchase costs; performs perturbation correction on the fractal characteristics based on these sensitive nodes to generate corrected load forecast results; maps the forecast results to the constraint structure to construct the correlation topology between load characteristics and electricity purchase costs; further extracts the cost sensitivity distribution, establishes a hierarchical mapping model, performs structured optimization, and outputs the electricity purchase plan. This invention improves the responsiveness of electricity purchase strategies to market changes and the level of cost control under complex market conditions.

This application provides a method for training an intelligent agent model for the electricity market, applicable to the field of artificial intelligence. The method includes: the intelligent agent model is a reinforcement learning model built on an actor-commentator network framework; the actions output by the intelligent agent model are input into an electricity market environment model to obtain the market environment state; wherein the actions are the declared electricity price and declared power, and the market environment state is the node price and winning bid power at the electricity market clearing point; the actor network of the reinforcement learning model is trained using a first loss function; wherein the function value of the first loss function is determined based on the values ​​of the main loss term and the regularization term; the main loss term represents the negative value of the Q-value corresponding to the action, and the regularization term represents the difference between the actual market electricity price and the declared electricity price; the Q-value corresponding to the action is determined by the commentator network in the reinforcement learning model based on the market environment state and the action.

This invention provides a multi-scenario adaptive virtual power plant control method and system for electric vehicle resources, comprising: determining the control demand parameters of the target electricity market; determining the personalized adjustable capability profile of electric vehicle charging piles; constructing a dynamic resource pool corresponding to the target electricity market based on the type of the target electricity market, the control demand parameters, and the personalized adjustable capability profile; generating personalized control instructions for electric vehicle charging piles and issuing the personalized control instructions to the corresponding electric vehicle charging piles; when a deviation between the actual control result and the differentiated control indicators is detected, dynamically generating correction instructions and issuing the correction instructions to the corresponding electric vehicle charging piles until the overall control objective is achieved. This invention can dynamically select charging pile resources according to the needs of different electricity markets and issue differentiated control instructions, thereby improving the market response efficiency and overall profitability of the virtual power plant.

This application discloses a method, apparatus, storage medium, and electronic device for cross-provincial electricity trading. The method includes: acquiring data on the electricity category, trading cycle, national mandatory plan electricity allocation ratio, trading electricity limit for each cycle, and inter-provincial tie-line channel stability limit for each cycle of cross-provincial electricity trading; acquiring a first electricity trading flow for each category of electricity trading between each trading cycle based on the aforementioned data; acquiring a second electricity trading flow for each category of electricity trading between the same trading cycle based on the aforementioned trading electricity limit for each cycle and the aforementioned inter-provincial tie-line channel stability limit for each cycle; and determining the value of untraded electricity that can enter other electricity markets based on the first and second electricity trading flows, thereby improving the efficiency of electricity market optimization and allocation.

This invention relates to a virtual power plant distributed energy aggregation and scheduling method and system, comprising the following steps: First, protocol adaptation and data collection are performed on various distributed energy resources through a unified resource access platform to obtain resource access data. Second, attribute extraction and filing are performed on each distributed energy resource based on the resource access data to obtain resource profile files. Third, the operating status and potential prediction of each distributed energy resource are performed in real time based on the resource profile files to obtain resource adjustable potential data. Fourth, based on the resource adjustable potential data and a preset electricity market price and grid dispatching requirements, a collaborative scheduling strategy is generated and instructions are issued for each distributed energy resource to obtain resource scheduling instructions. This invention solves the technical problem that traditional technologies neglect the actual adjustment needs of the grid at different times and fail to achieve complementary advantages and collaborative optimization among resources, resulting in low scheduling efficiency.

This application proposes a method, system, equipment, and medium for clearing and scheduling pumped-storage units participating in the spot market, considering the impact of head. The method includes: analyzing actual operating data of the pumped-storage power station and constructing relationship models between the unit's maximum pumping and generating output and the head; determining the head value of the unit at time period t, and calculating the unit's maximum pumping and generating output at time period t using the relationship models; obtaining the unit's application information for participating in the spot electricity market, and using the calculated maximum output to formulate multiple constraints; based on these constraints and the application information, performing spot electricity market clearing calculations to obtain the output plan for time period t+1; and performing iterative calculations to obtain the unit's output plan curve for each operating day. This method ensures that the output results conform to the actual operating conditions of the pumped-storage unit, improves the accuracy of the unit's output calculation results and clearing optimization results, and guarantees the reliability of the pumped-storage output plan execution.

This invention discloses an adaptive reduction method, system, device, and medium for regional mutual assistance power trading. The method includes: clearing the power market in chronological order to obtain initial trading power data for each market participant in each trading period; constructing a power reduction optimization model considering all uncertainty scenarios; solving the model to obtain reduced trading power data for each market participant in each trading period; using the reduced trading power data as the maximum tradable power limit for each market participant in each trading period, and re-clearing the power market for each trading period to obtain new trading power data; repeating the iteration until the difference between two clearing results is less than a set threshold or the maximum number of iterations is reached, and using the trading power data at this point as the final trading result. This invention maximizes the social welfare of regional mutual assistance while ensuring grid security, effectively solving the problems of trading efficiency and fairness under supply and demand fluctuations.

This invention relates to the field of intelligent big data analysis and decision support technology in the power market, specifically a big data analysis-based system for predicting the behavior of market participants in a power trading platform. The system includes: a data acquisition module, used to configure and execute real-time acquisition strategies for data filtering and parsing based on multi-source heterogeneous data feature analysis from the power trading platform, third-party data service providers, and enterprise internal management systems, to obtain a preliminary processed data stream; and to implement parallel transmission aggregation and transmission path optimization control on the preliminary processed data stream to obtain a raw dataset containing market transaction data, basic data of market participants, and external influence data. This invention achieves efficient processing and accurate decision support for the entire chain of power market transaction data through a systematic process of integrated data acquisition, intelligent quality optimization, multi-dimensional behavior prediction, and dynamic risk warning.

This invention discloses a decoupled take-or-pay settlement method adapted to the coordination of two-tier markets, comprising the following steps: establishing a decoupled take-or-pay settlement process description for two-tier markets; performing data processing; and establishing a decoupled take-or-pay settlement model for two-tier markets. This invention uses the existing settlement mechanisms and rules of inter-provincial and intra-provincial markets as a reference, analyzes the impact of factors such as settlement data differences, market boundary connections, deviation liability determination, and green electricity environmental rights allocation on the coordinated settlement of two-tier markets, and proposes a coordinated settlement method adapted to the construction of a unified national electricity market. By establishing a multi-level decoupled settlement model of headquarters-branch-provinces under a "two-stage, three-settlement" framework and performing collaborative optimization, a complete accounting system including contracted electricity settlement, deviation settlement, and green electricity environmental rights settlement is constructed, exploring the coordination mechanism between inter-provincial and intra-provincial markets in terms of timing, responsibility allocation, and cost balancing.

This invention provides an intelligent generation system and method for reverse trading strategies in the electricity sales business, relating to the field of electricity market technology. The invention performs modal decomposition on historical market price data and load demand data using an embedded modal correlation mapping mechanism to determine the price-load correlation relationship characterizing disturbance coupling. Based on historical reverse trading price data of market participants, it analyzes the evolution of the price game state under different risk preference scenarios. Combining the price-load correlation relationship and the price game evolution pattern, it constructs disturbance propagation paths, identifies key disturbance propagation paths, and generates a predicted price-load evolution trajectory. Furthermore, by combining real-time market transaction feedback data, it constructs a risk propagation topology structure for the key disturbance propagation paths, generating a reverse trading strategy to block risk propagation and effectively suppressing risk propagation in the electricity sales market.

Systems and methods are provided involving executing an optimizer using output power information of a renewable energy generator (REG) and market information, and executing an energy management system to manage the utilization of power generated by the REG based on the optimization. The REG is associated with a hydrogen production plant (HPP) for producing hydrogen, and the HPP is powered at least by the REG. The management of the power generated by the REG includes generating an electricity market offer to an electrical grid and a hydrogen market offer to an hydrogen distribution system. The system then dispatches REG power to the electrical grid and hydrogen to the hydrogen distribution system based on the generated market offers. REG output power typically fluctuates over time, as do other potentially relevant factors, such as consumer electricity demand, electricity transmission capacity, electricity market prices, hydrogen market prices, etc. The system can provide optimized control over the REG and HPP as conditions change over time.

The application discloses a kind of considering available transmission capacity's province's medium and long-term transaction rolling out clearing method and system, belong to electric power market field, including: obtaining the pre-bid clearing result of province's medium and long-term electric power transaction;The pre-bid clearing result of province's medium and long-term electric power transaction is input into the medium and long-term electric power market transaction optimization clearing model based on MVCG mechanism, and the transaction electric quantity and transaction electric price are obtained, complete province's medium and long-term transaction rolling out clearing, wherein the medium and long-term electric power market transaction optimization clearing model based on MVCG mechanism satisfies transmission network constraint, and the method and system realize the long-term transaction rolling out clearing of satisfying transmission network available transmission capacity.

This invention belongs to the field of electricity market trading and virtual power plant operation control technology. It discloses a rule-driven, fast-response trading strategy generation and simulation verification system for virtual power plants. This invention inputs the strategy into a verification environment that highly simulates the real market environment for closed-loop testing and performance evaluation, forming an iterative closed loop of "generation-verification-optimization". The system preferably combines a fast-solution variant of mixed-integer linear programming (MILP) with a rule engine, significantly improving response speed while ensuring strategy quality. By injecting domain knowledge into the decision-making process in the form of rules, combined with a fast optimization algorithm, this invention effectively solves the problems of slow response speed and poor interpretability of traditional methods, significantly improving the trading returns, strategy feasibility, and real-time response capabilities of virtual power plants participating in the electricity market.

The application discloses a carbon-aware collaborative optimization scheduling method and system for a virtual power plant, and belongs to the technical field of power system optimization operation and low-carbon scheduling. The method comprises the following steps: acquiring a multi-source input signal, wherein the multi-source input signal comprises an electricity market signal, a carbon market signal and real-time state data of distributed resources, wherein the carbon market signal comprises a real-time carbon price and a predicted value of system marginal carbon emission intensity in a future period; based on the real-time state data and the dynamic carbon signal, a resource unified digital image comprising an electricity adjustable capacity sub-model and a carbon emission reduction potential sub-model is constructed; a mixed integer linear programming model is established, taking the maximization of electricity-carbon comprehensive income as an objective and taking a conditional value at risk as a risk constraint, and is solved to generate an optimal scheduling plan; the optimal scheduling plan is executed by a real-time sensing-control module, and a device state is monitored in real time; when an abnormality is detected, a local compensation strategy is started, thereby forming a closed-loop control of "sensing-optimization-control-execution-feedback". The application realizes the collaborative optimization of economic benefits, environmental benefits and operation risks of a virtual power plant by deeply integrating a dynamic carbon signal and load flexibility, and ensures the robust execution of the strategy through a bottom-layer reliable control module, thereby improving the comprehensive benefits and operation reliability of the virtual power plant in an electricity-carbon integrated market.