184 results about "Economic dispatch" patented technology

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention relates to the technical field of power system optimization scheduling, and discloses a distribution robust opportunity constraint low-carbon economic scheduling method considering an N-1 safety criterion, and the method comprises the steps: building a wind-solar-energy-storage combined power generation system carbon transaction model which considers the bidirectional flowing of a carbon emission right, and taking the minimization of the total operation cost of the wind-solar-energy-storage combined power generation system as a target; establishing a low-carbon economic dispatching model considering the safe operation constraint of the power grid; aggregating wind and light output to establish a fuzzy set based on uncertainty moment information, establishing a distribution robust opportunity constraint economic dispatching model considering wind power uncertainty, and adopting a conditional value-at-risk theory to measure a system operation risk; on the basis, a power transmission line N-1 safety constraint rapid dynamic checking and adding method is provided based on branch circuit breaking distribution factors, and an economic dispatching model considering an N-1 safety criterion is established and solved. The power supply reliability of the system can be ensured under the condition of key line faults, and the solving efficiency and the practicability of the scheduling scheme are improved.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention relates to a multi-time-scale toughness scheduling strategy of a multi-energy complementary system under an extreme high temperature condition in optimal scheduling of a power system. In order to solve the problems of load rising caused by high temperature, derating of a source network and difficulty in guaranteeing power supply safety by traditional economic dispatching, a source-network-load-storage temperature effect model of photovoltaic, thermal power, a power transmission transformer and a load is constructed, and a day-ahead, day-intraday and real-time three-layer collaborative optimization framework is embedded; weighted unsupplied electric quantity is used as a toughness index, node vulnerability and load grade weight are combined, dictionary order optimization is adopted before the day, prediction deviation is corrected in a rolling mode within the day, toughness-oriented model prediction control and rolling supply stop window constraint are adopted in real time, and conventional unit, energy storage and layered demand response are cooperatively scheduled. Therefore, load loss is reduced and new energy is abandoned in an extreme high-temperature scene, and the power supply guarantee capability of key nodes and important loads and the overall toughness and economical efficiency of the system are improved.

Disclosed is an integrated energy system optimized dispatching method based on a variable time constant gradient algorithm. A Markov decision making process model is established based on an economic dispatching characteristic of an integrated energy system first, and a target optimization function is established. Then, a neural network is established and trained by applying a double-delay depth deterministic strategy gradient algorithm, effective experience is determined before updating a target network, and a variable time constant is set according to a reward value of a current round and a reward value of the last round of soft update. Finally, a trained intelligent agent is used for intra-day dispatching of the integrated energy system, so as to realize optimal economic cost operation of the integrated energy system.

The invention discloses a double-layer collaborative energy management and control method and system for a cold plate type liquid cooling data center, and belongs to the technical field of data center energy management. The method comprises the steps that upper-layer day-ahead economic dispatching is based on time-of-use electricity price and outdoor environment temperature prediction, and a server load scheme and a cooling system reference flow track are generated with the minimum total operation cost as the target; in lower-layer intraday dynamic regulation and control, model prediction control is adopted to track the trajectory, the load rate of a server and the rotating speed of a secondary pump are adjusted in real time, and it is ensured that the temperature of a chip is within the safety range of 45-70 DEG C; and the lower layer feeds back the actual chip temperature and energy consumption to the upper layer for online correction of dynamic thermodynamic model parameters to form closed-loop optimization. The system comprises a data acquisition module, a dynamic thermodynamic modeling module, an upper and lower layer optimization control module and an information interaction and feedback module. According to the invention, the overall operation cost and the PUE value of the data center are reduced on the premise of ensuring the temperature safety of the chip.

The invention belongs to the technical field of electric power engineering. The DC interconnected power distribution network optimal scheduling method based on converter control switching is used for solving the problems of power distribution network voltage out-of-limit, low scheduling efficiency and the like under the high permeability of new energy, VSC network following and VSC network construction dual-mode cooperative control is adopted, a photovoltaic-energy storage-VSC multi-level voltage stability support system is constructed, and the optimal scheduling of the DC interconnected power distribution network is realized. Dual-mode undisturbed switching is realized through virtual quantity calculation and proportion superposition; a cross-regional power mutual aid mechanism is established based on a direct current channel, and an electricity purchasing cooperative control system is established in combination with time-of-use electricity price. According to the method, collaborative optimization of voltage stabilization and economic dispatching is realized, the new energy consumption capability is improved, the power purchase cost of a superior power grid is reduced, and the method is suitable for a'source-grid-load-storage 'collaborative dispatching scene.

The invention discloses a method, a system, equipment and a medium for energy management and economic dispatching optimization of an optical storage and charging system, and relates to the technical field of optical storage and charging. The comprehensive operation state model represents an operation rule and an economic index of the optical storage and charging system in a future scheduling period; based on the comprehensive operation state model, solving a multi-objective optimization problem to obtain a Pareto optimal solution set considering economy, environmental protection property and service quality; and selecting an optimal scheduling strategy from the Pareto optimal solution set by adopting a multi-agent equilibrium game mechanism so as to determine a final energy management and economic scheduling scheme. According to the method, the problems of difficulty in multi-target coordination, insufficient electricity price response capability and user behavior modeling simplification in the existing energy management technology of the optical storage and charging system are solved, so that the overall benefit of the system is better, the method can adapt to various market mechanisms and different meteorological conditions, and the application scene is wide.

The invention provides a transformer area two-stage distribution robust line loss optimization method considering wind and light uncertainty, and belongs to the technical field of new energy. Firstly, a day-ahead-real-time two-stage line loss optimization method is constructed, and in the day-ahead stage, a K-means clustering algorithm and a conditional generative adversarial network are combined to generate a multi-scene sample set capable of reflecting the uncertainty of wind and light output; in a real-time stage, based on a day-ahead optimization result and updated output information, a multi-scene distribution robust optimization model is utilized to realize joint correction of line loss and economic dispatching; in the optimization solution process, a forward-backward substitution method is adopted to embed the load flow calculation model so as to accurately reflect the line transmission loss of the transformer area power distribution network; secondly, a distributed robust optimization method based on scene generation is constructed, historical wind and light data are utilized, a diversified scene set is generated through K-means clustering and a conditional generative adversarial network, and distributed robust optimization planning and correction are carried out in different scenes.

The invention discloses a building light storage energy economic scheduling and power compensation management system and method, and relates to the technical field of building energy management, and the method comprises the following steps: obtaining scheduling data in a future preset scheduling period, carrying out the prediction analysis based on the scheduling data, and generating a macroscopic charging and discharging scheduling strategy; the method comprises the following steps: acquiring a sky image sequence by using a visual sensor, analyzing the movement of a cloud cluster based on the sky image sequence to predict the descending trend of photovoltaic power, and sending a pre-compensation instruction to an energy storage converter and controlling the energy storage converter to charge and discharge before the actual descending of the photovoltaic power. According to the method, the descending trend of the photovoltaic power can be accurately predicted in advance, the pre-compensation instruction is sent to the energy storage converter before the actual falling of the power, the peak-valley electricity price arbitrage income is maximized through the combination of high-precision prediction and accurate pre-compensation, and the optimization and improvement of the overall performance of the building optical storage system are further realized.

The invention relates to a distributed intelligent power grid system and method based on electricity carbon quantification and source network load storage cooperation, and belongs to the technical field of energy internet and intelligent power grids. Comprising an integrated distributed new energy power generation unit, a flexible load cluster, an energy storage system and a power grid interaction interface; the electric carbon quantitative analysis module is used for calculating dynamic carbon factors of each node in real time, constructing a carbon flow tracking model and quantifying carbon footprints of a source network load storage whole link; the distributed intelligent decision engine is deployed on an edge computing node based on a block chain consensus mechanism and executes local autonomous optimization and global collaborative scheduling; the multi-time scale optimization module is used for coupling carbon constraint conditions and realizing layered optimization of day-ahead economic dispatching, intra-day rolling correction and second-level quick response; through second-level carbon sensitive scheduling of a multi-time scale optimization module, and in combination with the fluctuation stabilizing capability of the energy storage system, the new energy consumption rate is increased to 92% or above.

The invention discloses a comprehensive energy system source-load collaborative low-carbon economic dispatching method based on extended carbon emission flow. The method comprises the following steps: constructing a comprehensive energy system extended carbon emission flow model based on an energy hub; constructing a combined operation model of a wind turbine generator, a carbon capture unit and power-to-gas based on carbon flow; a low-carbon demand response mechanism based on carbon potential is designed, and the carbon reduction potential of the load side is released; a carbon flow-based master-slave game framework is constructed, and the high-carbon attribute of the system is improved; and designing a source-load cooperative operation method under a carbon flow view angle to realize low-carbon economic operation of the system. According to the method, an extended carbon emission flow model of the comprehensive energy system is constructed, a combined operation model of a wind turbine generator, a carbon capture unit and power-to-gas is constructed based on the extended carbon emission flow model, and the coupling characteristic of the source side of the system is improved; a low-carbon demand response mechanism based on carbon potential is designed, and the perceptibility of a user to carbon difference is improved; source-load cooperative operation is realized based on the master-slave game, and the low-carbon economic level of the system is improved.

The invention relates to the technical field of large-scale power system safety guarantee and intelligent scheduling, and discloses a new energy power system optimization scheduling method and system, and the method comprises the steps: carrying out the internal processing steps before economic scheduling, and carrying out the self-adaptive correction of a frequency change rate safety threshold value based on the overall completeness of a power defense system, real-time data closed-loop verification is carried out based on a wide-area measurement system, and an inertia contribution constant of a started unit is selected; and then generating a minimum system inertia base line constraint based on the corrected frequency change rate safety threshold and the inertia contribution constant selected after verification, and implanting the minimum system inertia base line constraint into the economic dispatching optimization model for execution. According to the method, a closed-loop information interaction path is established between power system safety analysis logic and an economic dispatching model, and verified physical safety requirements are preposed as constraint conditions which must be obeyed by the economic model.

The invention relates to the technical field of electricity price prediction, in particular to an electricity market long-period simulation-based medium and long-term price prediction method, which comprises the following steps of S1, market basic data acquisition: acquiring whole market basic data information; s2, unit commitment model construction and day-ahead simulation: constructing a unit commitment model considering power grid security constraints; s3, economic dispatching model construction: constructing an economic dispatching model considering power grid security constraints; s4, node marginal electricity price and load flow calculation: node marginal electricity price calculation and load flow calculation are carried out; s5, uncertainty factor evaluation: the uncertainty factors existing in the electricity market are evaluated; s6, analyzing technical performance and economic indexes: analyzing statistics of various technical performance and economic indexes of the system; according to the method, the stability and the reliability of an enterprise under an emergency situation are enhanced, so that the enterprise can better adapt to market changes, and the scientificity and the accuracy of decision making are improved.

The invention discloses an optimal preset output solving method based on a minimum-maximum criterion and global robustness, and relates to the technical field of power system optimization scheduling. An existing scheduling method mainly focuses on robustness improvement of a local scene, cost deviation controllability of a global extreme scene is not achieved, the solving efficiency of a multi-layer optimization model is low, and the real-time scheduling requirement of an actual power system is difficult to meet. Establishing a power system economic dispatching minimum cost objective function, and establishing a linearized KKT condition constraint objective function; presetting a segmented cost function and a segmented constraint condition, and constraining the linearized preset cost function by using linear constraint; establishing an optimization target according to the minimum cost target function and the linearized preset cost function, processing the optimization target into a multi-layer optimization model, converting the multi-layer optimization model into a double-layer optimization model, and solving the double-layer optimization model by using a Benders decomposition method to obtain optimal solutions including preset wind power output, preset photovoltaic output and thermal power generating unit output. The method is used for obtaining power system output.

The invention provides an intelligent disk monitoring method and device based on thermal power plant circulating pump economic dispatching, and aims to solve the problems of dispatching response lag, scattered monitoring, nonstandard operation and the like in traditional circulating pump operation. The system comprises a data acquisition module, an economic dispatching decision module, a state monitoring module, a fault early warning module, a dispatching instruction execution module and a man-machine interaction module, the optimal circulating pump operation mode is automatically matched by acquiring the parameters such as the Yangtze River water temperature, the unit load and the condenser vacuum in real time, and the switching operation is executed according to the standardized step sequence. Meanwhile, the system carries out full-dimension state monitoring on a circulating pump, a butterfly valve, circulating water and a vacuum system, and fault early warning and intelligent pushing are achieved in combination with abnormal parameter correlation analysis. A large amount of hardware does not need to be added, the circulating pump power consumption can be remarkably reduced, the operation safety and the automation level are improved, and the method is suitable for intelligent monitoring and optimal scheduling of various thermal power generating units.

The application discloses a kind of considering multiple uncertainties mixed energy storage virtual power plant low-carbon economic dispatching method and system, it is related to electric power system optimization scheduling field, including: design pumped storage-flywheel-electrochemical mixed energy storage layered collaborative control mechanism, establish carbon capture-electricity to gas equipment dynamic collaborative operation model and green certificate-carbon trading dynamic coupling model, construct day-ahead-intra-day two-stage rolling optimization framework, develop mixed energy storage capacity optimization configuration model.The application can fully tap the complementary characteristics of multi-time scale energy storage and the collaborative potential of low-carbon technology, realize the dual promotion of virtual power plant economy and environmental protection.

The invention discloses a multi-time scale coupled source-storage collaborative economic dispatching method and system, relates to the field of power system dispatching, and solves the problem of low dispatching economy of a power system dispatching scheme. According to the day-ahead scheduling optimization in the embodiment of the invention, the hidden cost of deep peak regulation and high-frequency charging and discharging can be quantified through the deep peak regulation coal consumption function of the thermal power generating unit and the charging and discharging depth-service life attenuation mapping table of the energy storage equipment; the intra-day rolling optimization dynamically adjusts the energy storage behavior based on the real-time electricity price, so that the scheduling plan can be matched with the market fluctuation, and the energy storage income is improved; by combining the two points, the operation characteristics of the power system under the complex working condition can be reflected more accurately, and the scheduling economy of the finally obtained optimal scheduling scheme is improved.

The application discloses a multi-time scale hierarchical scheduling method for a combined heat and power system containing electric vehicles, and the system comprises a micro gas turbine unit, an absorption refrigeration unit, a ground source heat pump unit, a wind turbine unit, photovoltaic, a battery, a super capacitor and a heat storage device. The optimization method contains two time scales. In the day-ahead stage, economic scheduling is carried out according to the day-ahead predicted power value of wind power and photovoltaic and the day-ahead predicted value of load power in the system, and a target function and optimization constraint condition are established, and the result is used as a reference for the day-ahead optimization. In the day-ahead stage, a two-layer rolling optimization model is considered, a target function and a constraint are established, and a model predictive control (MPC) rolling optimization layer corrects the result of the upper layer according to real-time predicted power information and non-planned behavior of the electric vehicle (EV). The application adopts a multi-time scale hierarchical optimization strategy, real-time tracks the optimal scheduling strategy of the combined heat and power system containing electric vehicles, and realizes a win-win situation between the electric vehicle owner and the micro-grid operator.

The application provides an economic dispatching method and device based on the working condition and frequency safety constraint of a thermal power unit, equipment and a storage medium, and relates to the technical field of power system operation and optimal dispatching. The method comprises the following steps: obtaining the operation data of a power system and the actual operation working condition data of a thermal power unit; quantifying the frequency modulation relationship between the primary frequency modulation up-regulation capability of the thermal power unit and the actual operation working condition of the thermal power unit based on the permeability change data and the actual operation working condition data of the thermal power unit; constructing a dynamic frequency safety constraint model of the power system based on the frequency response model of the power system and the frequency modulation relationship; constructing an economic dispatching model with economic optimization as the target and embedded frequency safety constraint based on the traditional constraint condition and the dynamic frequency safety constraint model, so as to generate an economic dispatching operation strategy according to the economic dispatching model. The present disclosure realizes the significant improvement of the stability and economy of the power system by comprehensively considering the frequency safety constraint and the working condition of the thermal power unit.

The present application relates to a kind of large air conditioner " electricity-storing-supply " integrated comprehensive energy regulation method, belong to microgrid economic dispatch field, including the following steps: step 1: with the energy storage material cost of energy storage water tank material in energy storage air conditioning system, conventional unit generation cost, unconventional unit generation cost, operating environment cost as objective function, model is established;Step 2: establish the operating constraint condition in scheduling period;Step 3: the model and constraint condition are brought into a kind of large air conditioner " electricity-storing-supply " integrated comprehensive energy regulation method;Step 4: the optimal allocation scheme of system is obtained after continuous iteration, so that microgrid is in stable operation while making economy further optimized.

The invention discloses a micro-grid energy storage multi-stage economic dispatching method, system and device based on differential evolution and a storage medium, solves the problem of how to improve micro-grid energy storage dispatching efficiency, and belongs to the field of power system operation and control. Comprising the following steps: generating a forced charging baseline instruction of which the system net power accords with a photovoltaic surplus period threshold; setting a power upper limit to initialize population individuals of a differential evolution (DE) algorithm, and generating a test vector; superposing the forced charging baseline instruction and the test vector to synthesize a preliminary attempt power, correcting a capacity boundary exceeding a capacity constraint, and outputting a first correction power; superposing the first corrected power and the system net power to synthesize a system net load, and outputting second corrected power when the corrected system net load is greater than a power boundary of a threshold value; constructing a high-fidelity objective function to execute DE optimization, and outputting an optimal individual to perform microgrid energy storage scheduling control; according to the method, the nonlinear battery life loss and the refined peak-valley arbitrage economy are taken into consideration under the condition that the physical operation safety of the bottom layer is guaranteed.

This invention relates to the field of thermal power plant technology, and provides a method and system for optimizing the operation of thermal power plants based on digital twin technology, including the following steps: Step S1, acquiring the physical mechanism and historical operating data of the thermal power plant's generator units, processing and storing the data; Step S2, establishing a digital twin model of the thermal power plant's generator units based on the physical mechanism and historical operating data. This invention, by establishing a digital twin model linked to the optimization algorithm, realizes the dynamic transformation of economic dispatch in thermal power plants, ensuring that load allocation schemes are not only based on the actual capacity of the units, but also predict safety risks during execution, preventing the optimization results from deviating from the actual optimal solution due to model lag, and improving the economy and safety of power plant operation. By introducing marginal cost range constraints based on equipment health status and a closed-loop optimization mechanism, adaptive economic dispatch under safety constraints is achieved.

The invention discloses a flexible coordination control system and method for an AC-DC hybrid power distribution network, and particularly relates to the technical field of power systems and automation thereof, and the method comprises the steps: building a hybrid model based on the fusion of a graph neural network and a physical law, and updating a digital twin in real time, so as to calculate the stability margin of the system; according to a stability margin dynamic switching load and a time scale and a precision focus of new energy prediction, heterogeneous resources are dynamically aggregated into a logic virtual power plant based on real-time topology and stability requirements, and through a three-layer architecture of a rule layer, an optimization layer and an autonomous layer, extreme faults, economic dispatching and millisecond autonomous control are cooperatively processed, so that the stability of the power plant is improved. According to the invention, the problems that the multi-form resources of the AC / DC hybrid power distribution network are difficult to coordinate and the control response is lagged are solved, and the full-link intelligence and active safety protection from perception, prediction, aggregation, decision-making to execution are realized.

This invention discloses an energy management and control method and system for off-grid photovoltaic systems based on hydrogen energy storage. Addressing the problems of spatiotemporal imbalance between energy supply and demand, insufficient coordinated control of the electric-hydrogen hybrid energy storage, low system energy self-sufficiency, and poor operational stability in off-grid photovoltaic systems, it constructs a closed-loop energy flow model of photovoltaic-electrolyzer-hydrogen storage tank-fuel cell-load and designs a three-layer hierarchical distributed control system. The upper layer performs predictive economic scheduling, the middle layer completes real-time power allocation with rolling optimization, and the lower layer achieves device-level adaptive precision control. An intelligent control platform is built by integrating edge computing and a cloud platform, and a graded fault-tolerant mechanism for abnormal operating conditions is set up. This invention achieves intelligent management and control of the entire generation, storage, and utilization chain, significantly improving the system's energy self-sufficiency, operational economy, and safety. It supports modular expansion and is adaptable to various off-grid energy supply application scenarios.

The application discloses a multi-target-oriented power distribution network safety domain operation point screening method, medium and equipment, belongs to the field of power system operation and optimal control, and comprises the following steps: firstly, constructing a static safety domain containing a power flow equation and multiple constraints based on power distribution network nodes, branch information and safety criteria; secondly, accurately obtaining a safety domain boundary through adaptive direction search and error correction; thirdly, generating a candidate operation point by Monte Carlo sampling, combining economic and reliable indexes, and obtaining a Pareto optimal solution set through fast non-dominated sorting; finally, screening an optimal operation point considering safety, economy and reliability through fuzzy multi-attribute decision and decision maker preference analysis; the application realizes three-dimensional target collaborative optimization, is high in calculation efficiency and strong in robustness, can adapt to real-time optimization requirements of high-proportion distributed power distribution networks, and can be widely applied to active power distribution network safety evaluation, energy storage configuration and economic dispatching scenes.

The application discloses a two-stage economic dispatching method for an island micro-grid containing multiple types of loads, and designs a two-stage optimization control algorithm composed of a power generation unit dispatching and load dispatching, the micro-grid contains renewable energy, traditional energy, a battery, important loads, transferable loads, interruptible loads, and considers wind and light abandonment operation under oversupply condition, in the first stage (power generation unit dispatching stage), important loads, transferable loads, interruptible loads and wind and light abandonment amount are normalized into power supply gap amount (or power amount overflow amount), a dynamic model formed by augmented states such as state of charge and power supply gap is established, the total operation cost of each device of the micro-grid is dynamically optimized, the traditional energy power generation amount, the battery charging and discharging amount and the power supply gap amount are calculated, in the second stage (load dispatching stage), the power supply gap value calculated in the first stage is distributed to the transferable loads, the interruptible loads and the wind and light abandonment amount through static optimization.

The invention discloses a multi-time-scale microgrid economic dispatching method based on relative entropy inverse reinforcement learning, and the method comprises the steps: obtaining a day-ahead dispatching plan and a tie line dispatching plan according to a day-ahead prediction information set of a microgrid; superposing disturbance on the day-ahead scheduling plan to carry out dimension expansion, and generating an expert strategy for inverse reinforcement learning; according to the expert strategy, a reward function is constructed by adopting a relative entropy inverse reinforcement learning algorithm, and the reward function is obtained by minimizing the relative entropy of trajectory distribution and solving a corresponding Lagrange duality function; according to the reward function, adopting a depth deterministic strategy gradient algorithm to train an intra-day scheduling agent to obtain a trained intra-day scheduling model; obtaining ultra-short-term prediction information based on the tie line scheduling plan; and inputting the ultra-short-term prediction information into an intra-day scheduling model to obtain an intra-day scheduling strategy.

The invention relates to the technical field of energy management, and particularly discloses an energy management method and device for a port shore power system, equipment and a medium. The power supply service protocol at least comprises a load base line corresponding to the ship, a load adjustable power range, a power supply quality threshold value and load adjustment excitation information; according to the power supply service agreement, the port energy state information and the security constraint, periodically solving an economic dispatching optimization model to obtain a target output set value of each powered ship; and according to the target output set value and the actual measurement value of the port ship, performing dynamic adjustment on the power supply output of the port shore power system so as to improve the operation economy of the port shore power system through an economic dispatching optimization model, and ensuring effective implementation of the target output set value through dynamic adjustment. Therefore, effective implementation of the scheduling strategy is ensured.

This invention relates to a low-carbon economic dispatch method for integrated energy systems that considers the variable operating conditions of energy conversion equipment, belonging to the field of power systems. The method includes the following steps: S1: Combining the variable operating conditions of energy conversion equipment in the integrated energy system with static energy hubs to construct a dynamic energy hub state transition model for the integrated energy system; S2: Combining a full life-cycle analysis method with a carbon quota trading mechanism to construct a carbon trading model for the integrated energy system; S3: Based on the dynamic energy hub state transition model and the carbon trading model, constructing a low-carbon economic dispatch model for the integrated energy system that considers the variable operating conditions of energy conversion equipment, and solving for the low-carbon economic dispatch strategy that considers the variable operating conditions of energy conversion equipment. This method can provide technical support for the operation and dispatch of integrated energy systems, and has advantages such as low system operating costs, low carbon emissions, and flexible and reliable operation.