13 results about "Energy pricing" patented technology

The invention belongs to the technical field of power system optimization scheduling, and discloses a mathematical model dual-drive-based port intelligent scheduling method, which comprises the following steps of inputting real-time operation data of a port logistics system and an energy system; on the basis of a deep reinforcement learning model, operation data is adopted for training, a state-action-reward mapping relation is established, and a data-driven preliminary scheduling strategy is generated; based on the operation data, constructing a model-driven traffic distribution-user balance optimization model, and obtaining an energy pricing strategy capable of minimizing the operation cost; constructing a multi-agent collaborative decision framework to coordinate a preliminary scheduling strategy and a traffic distribution-user equilibrium optimization model, and realizing iterative optimization of dual-drive strategy collaboration; dynamically selecting an optimal sub-heuristic strategy by adopting a dual deep Q network structure based on an iterative optimization result; the problems of insufficient strategy generalization ability and poor dynamic environment adaptability in the prior art are solved.

The invention relates to a multi-body robust optimization scheduling method based on a bidirectional master-slave game, and belongs to the field of comprehensive utilization of energy. A bidirectional master-slave game model is provided, an integrated energy system operator (IESO) is used as a leader to optimize energy pricing, an energy storage operator (ESO) is used as a secondary leader, a load aggregator (LA) is used as a follower, and each main body formulates an energy transaction strategy according to the own state and benefit target to optimize the internal running state of the system, so that various load requirements are met. Uncertain factors are added into a proposed bidirectional master-slave game model, the uncertainty of wind and light output and electricity price of an electricity market is considered, a safety constraint economic optimization model under the worst condition is established, and the problem is converted into an MILP formula by using a strong duality theory. And finally, solving the proposed bidirectional master-slave game model by adopting a golden section method, only exchanging limited energy quantity and price information during transaction, and protecting data information privacy of each subject.

The present invention discloses systems and methods for optimized liquid cooling of data centers, featuring trickling filter towers for and biological oxygen demand (BOD) reduction in support of supplying non-potable water for evaporative cooling. A combination of e-pumps, flow sensors, and advanced controllers ensures continuous adaptation to fluctuating compute loads, weather conditions, and time-of-use (TOU) energy pricing. The approach mitigates reliance on remote data center load shifting, reduces operational costs, and preserves local water and energy resources. The invention also tolerates power fluctuations and minimal flow conditions, safeguarding critical cooling functions and biofilm viability with minimal energy consumption. As a result, data centers benefit from a resilient, efficient cooling infrastructure that integrates wastewater treatment and evaporative cooling, sustaining performance even under significant heat loads or resource constraints.

A computer implementation method for operation optimization of an industrial plant (500) is proposed, the industrial plant (500) including at least one hydrogen producer (510) and at least one hydrogen consumer (520), the industrial plant (500) being coupled to at least one electrical energy source (600). The method includes: receiving, at a computing system (100) values ​​of operating parameters of an industrial plant (500), the operating parameters including at least: hydrogen production of at least one hydrogen producer (510), hydrogen consumption of at least one hydrogen consumer (520), energy production of the industrial plant (500), energy consumption of the industrial plant (500), energy availability via at least one energy source (600), and energy pricing; calculating a predicted time series of the values ​​and / or boundaries of the operating parameters of the industrial plant (500); and, by means of an optimization module (124) of the computing system (100), determining one or more optimal operating setpoints for optimizing the operation control of the industrial plant (500) in at least one aspect of energy consumption and operating costs, based on the received values ​​of the operating parameters and the calculated predicted time series of the values ​​and / or boundaries of the operating parameters of the industrial plant (500), wherein the one or more optimal operating setpoints are determined based on at least one degree of freedom of the industrial plant (500).

The invention relates to the technical field of energy science, in particular to a comprehensive energy pricing method and device considering source-load interaction, and aims to solve the problem that a multi-energy demand side pricing mechanism does not fully consider a master-slave game relationship between an energy aggregator and a terminal energy consumption load and an energy aggregator cooperative game relationship of sharing adjustment capability. The method comprises the following steps: constructing an energy aggregator-load master-slave game model by taking the maximum operation income of an energy aggregator and the minimum energy consumption cost of a terminal energy consumption load as targets, and constructing an energy aggregator cooperative game model considering a load demand side adjustment response capability sharing mode; acquiring model input data including market clearing information, energy aggregator own equipment information and terminal energy consumption load characteristic information; and substituting model input data into the model, solving by adopting a DE-GWO hybrid optimization algorithm, and outputting a dynamic retail electricity price strategy of the energy consumption load of the terminal according to a solving result.

The present invention discloses an energy pricing method for an electric-gas integrated system. The method establishes a day-ahead market clearing model for the electric-gas integrated system by considering energy contracts and using the power network constraints, natural gas network constraints, and electric-gas integrated network coupling constraints as the constraint conditions, and taking the minimization of the sum of the power purchase costs of the power grid company and the gas purchase costs of the natural gas company as the objective function. Then, through this day-ahead market clearing model, the sum of the power / gas purchase costs of other members is calculated when each member participates and does not participate in the day-ahead market clearing, and the change in the sum of the power / gas purchase costs of other members is used as the value of this member to formulate the energy price. In the above technical solution, the energy operator formulates the energy price mechanism based on the values of each member obtained by the above method, which can prompt market members to report true prices in the bidding in order to achieve their maximum interests, and ensure the effective dispatching and stable operation of the power market.

The present invention is applicable to the technical field of capacity market pricing. Provided are a multi-energy capacity market pricing method and system taking power generation capacity adequacy into consideration. The method comprises: constructing a first model with the objective of maximizing the revenue of power generation companies in a capacity market involved in power generation types of thermal power generation, wind power generation, photovoltaic power generation, hydropower and large-scale energy storage power stations; determining active power output constraints, total capacity constraints and power transmission line constraints of thermal power units, wind farms, photovoltaic power stations, hydropower units and energy storage power stations; constructing a second model with the objective of maximizing the difference between the consumer surplus and the cost of the power generation companies in a day-ahead spot trading market; determining a generator unit power constraint and a consumer energy constraint; using an improved genetic algorithm to optimize and solve the first model; and on the basis of a dynamic programming method based on a duality theory, optimizing and solving the second model, so as to obtain a clearing price corresponding to an active power capacity of the power generation companies. Therefore, the effective operation of a power market is improved.

A smart energy platform for controlling energy consumption at a property, the smart energy platform configured to: (i) receive an input from a user associated with the property indicating a maximum comfort temperature and a minimum comfort temperature of the property; (ii) receive property data; (iii) retrieve weather data; (iv) determine property usage; (v) receive energy pricing data from an energy company; (vi) build a model based upon the input from the user, the property data, the weather data, and the property usage; (vii) input the energy pricing data and the weather data into the model; (viii) output an energy schedule for the property to maintain a temperature of the property within the maximum comfort temperature and the minimum comfort temperature at a minimum cost to the user; and / or (ix) control the temperature of the property based upon the energy schedule for the property.

This invention discloses a low-carbon optimized scheduling method for an integrated energy system with load response under the influence of flexible energy pricing. The method comprises: utilizing carbon flow tracking to quantify carbon emissions of network nodes in real time; pricing integrated energy based on carbon trading costs and load energy demand to construct an integrated energy pricing model; establishing an integrated demand response model that normalizes the internal price of the integrated energy system using time-of-use electricity prices as a benchmark value; and solving the constructed low-carbon optimized scheduling model for the integrated energy system with load response under the influence of flexible energy pricing. The method proposed in this invention improves the economic efficiency and carbon emission level of the integrated energy system while enhancing the integrated demand response of the integrated energy system.

A method (100) for dispatch planning of an electric vehicle (EV) fleet is disclosed, which optimizes route assignments and charging schedules. The method comprises obtaining fleet, route, and energy pricing information, including initial states of charge (SoC) and required SoC for each route. For each route, the method involves determining a pre-trip SoC and a post-trip SoC for feasible EVs, selecting an EV based on predefined criteria, and mapping it to the route. A charging schedule is determined using a charging duration factor (CDF) which is inversely proportional to average energy costs. The method outputs a dispatch plan with route mappings and the charging schedule. This approach balances maximizing fleet utilization with minimizing charging costs, offering a scalable solution for efficient EV fleet management.

The invention relates to the technical field of comprehensive energy, and discloses an electricity-heat-cold flexible mutual-aid RIES cooperative scheduling method, system and device for a single large load and a medium. The method comprises the following steps: constructing a flexible load transfer strategy, and carrying out prospective energy storage by using time sequence characteristics of heat energy and cold energy; and cross-energy mutual aid is started under an extreme load, and electricity-heat-cold two-way support is realized through energy conversion equipment. The system takes cross-energy mutual aid as a core, and integrates energy production, conversion, storage and load links, and sensor network real-time monitoring and coordination. Establishing a multi-subject collaboration framework covering energy manufacturers, managers and an integrated energy system, setting constraint conditions and optimizing a multi-subject target; and introducing a Great Wall optimization algorithm, solving a system model and optimizing energy pricing to obtain an optimal collaborative scheduling result. The operation toughness, economical efficiency and energy utilization efficiency of the system under a single large load are improved, the operation cost and carbon emission are reduced, and safety and stability of the system are guaranteed.

The present invention discloses systems and methods for optimized liquid cooling of data centers, featuring trickling filter towers for and biological oxygen demand (BOD) reduction in support of supplying non-potable water for evaporative cooling. A combination of e-pumps, flow sensors, and advanced controllers ensures continuous adaptation to fluctuating compute loads, weather conditions, and time-of-use (TOU) energy pricing. The approach mitigates reliance on remote data center load shifting, reduces operational costs, and preserves local water and energy resources. The invention also tolerates power fluctuations and minimal flow conditions, safeguarding critical cooling functions and biofilm viability with minimal energy consumption. As a result, data centers benefit from a resilient, efficient cooling infrastructure that integrates wastewater treatment and evaporative cooling, sustaining performance even under significant heat loads or resource constraints.

The application provides a kind of based on carbon certificate coupling integrated energy system planning configuration method and electronic equipment, it is related to integrated energy system planning configuration technical field.The method comprises: obtaining the multi-dimensional operation data of integrated energy system and generating dynamic carbon quota;Obtain supply and demand and fluctuation data of green certificate market and generate dynamic green certificate price;Based on dynamic carbon quota and dynamic green certificate price, cross-market conversion settlement is carried out to generate carbon certificate coupling boundary condition;Based on carbon certificate coupling boundary condition, solve multi-agent game model, output equipment configuration capacity and energy pricing strategy.The present application realizes the accurate quantification of environmental rights and interests value and the dynamic balance of multi-agent interests, achieves the global collaborative optimization of integrated energy system physical consumption and low-carbon economy.