953 results about "Power balancing" patented technology

The invention discloses an optimal scheduling method and system for a wind-light-hydrogen storage micro-grid, and relates to the field of wind-light-hydrogen storage micro-grids, and the method comprises the steps: obtaining multi-source time sequence data, and carrying out the combined denoising and dynamic time alignment to generate a standardized input sequence; adopting a quantile regression model fused with a space-time attention mechanism to output a power prediction interval of wind and light output and load demand in a future time period; constructing a layered multi-objective optimization model; an improved adaptive particle swarm optimization algorithm is adopted to solve the layered multi-objective optimization model, and an equipment scheduling instruction set is generated; and dynamic correction is carried out, and micro-grid instruction distribution is carried out according to the equipment response priority. According to the method, multi-target conflicts such as power balance, equipment loss and energy efficiency are effectively balanced through multi-source data efficient preprocessing, space-time joint prediction interval generation and hierarchical multi-target optimization, and efficient and stable operation of the wind-light-hydrogen storage micro-grid is achieved.

The invention relates to the field of power distribution networks, in particular to a power distribution network voltage collaborative autonomous method and system based on dynamic partition. The method comprises the following steps: acquiring electrical measurement data and network topology parameters of distributed nodes of a power distribution network, and generating a characteristic state set representing the operation state of a system; performing dynamic subarea division based on node voltage coupling strength and power balance constraint to obtain a dynamic subarea set with an autonomous boundary; each partition control main body independently solves a voltage regulation objective function of the partition according to an autonomous boundary, and generates a partition autonomous control strategy; and boundary interactive iterative coordination is carried out between adjacent partitions, and a global optimal voltage cooperative control instruction is generated and executed. According to the method, the problems that partition division is not matched with the operation state, and partition collaboration is insufficient are solved, unification of partition autonomy and global optimization is achieved, and the real-time performance and accuracy of voltage regulation and control of the power distribution network are remarkably improved.

The invention discloses a virtual power plant global optimization scheduling method, system and device based on cloud edge collaboration, and a storage medium, and belongs to the technical field of power system scheduling. The method comprises the following steps: based on a cloud edge coordinated regulation and control framework comprising a cloud layer, an edge layer and an end side layer, taking minimization of the total operation cost of a system as a target, comprehensively considering a power balance constraint, a main network interaction constraint, a distribution network transmission constraint, a distributed resource operation constraint, an energy storage equipment constraint and a renewable energy consumption constraint; establishing a global optimization scheduling model; edge collaborative optimization is realized by adopting an alternating direction multiplier method, a global coupling problem is decomposed into local optimization sub-problems and a cloud coordination problem of each region, and aggregation power information is sent to the cloud after the local optimization sub-problems are solved in parallel in each region; and the cloud performs global coordination optimization to generate an optimal scheduling strategy, and issues a scheduling instruction to the edge layer to control the actual operation of the distributed power supply, the energy storage equipment and the controllable load, thereby realizing the collaborative optimization scheduling of the virtual power plant. The problems that in virtual power plant large-scale distributed resource coordination optimization, calculation complexity is high, communication burden is heavy, and real-time performance and global optimality are difficult to consider at the same time are effectively solved.

The invention relates to the technical field of electric power intelligent management, and discloses a cooperative scheduling method and system for a virtual power plant, and the method comprises the following steps: S1, collecting the real-time data of each distributed power supply, each load and an energy storage system in the virtual power plant, and carrying out the ultra-short-term prediction, and obtaining a prediction parameter; s2, dynamically calculating the dynamic operation boundary of the energy storage system based on the real-time state of the energy storage system; s3, on the day before the current operation day, generating a pre-scheduling plan through collaborative decision making of a multi-target fuzzy satisfaction function; s4, in the current running day, taking the pre-scheduling plan as a reference, updating boundaries and prediction parameters in a rolling manner, and generating a real-time scheduling instruction through model prediction; and S5, monitoring the deviation between the actual output of each resource and the real-time scheduling instruction in real time, and when the deviation exceeds a threshold value, starting a collaborative deviation compensation mechanism to carry out power balance. According to the invention, fine cooperative scheduling of different types of distributed resources can be realized in a complex environment with high uncertainty.

The invention provides a smart power grid dispatching optimization method based on adaptive evolution control, which comprises the following steps: performing power network graph modeling and standardization processing on a power network to generate a standardized graph structure; constructing a Markov decision framework of power grid dispatching based on the standardized graph structure; performing interactive training under a Markov decision framework to generate a preliminary scheduling strategy meeting power balance constraints; taking the preliminary scheduling strategy as an initial population, and generating a global optimization scheduling scheme through fitness function evaluation and multi-mode crossover mutation operation; and deploying a global optimization scheduling scheme to an actual power grid, and dynamically updating strategy network parameters based on node loads, power generation output and frequency data acquired in real time. According to the method, the power grid dispatching strategy can be dynamically adjusted in real time, consumption and dispatching of new energy are optimized, and the adaptive capacity of the power grid to different loads and power generation fluctuations is improved.

The invention relates to the technical field of power dispatching, in particular to a power distribution network photovoltaic energy storage collaborative optimization dispatching decision-making system based on big data and artificial intelligence. Comprising a source network load storage full-dimension data acquisition unit; a multi-source heterogeneous data fusion processing unit; the dynamic multi-target intelligent optimization decision-making unit is used for constructing a power distribution network photovoltaic energy storage collaborative scheduling strategy through an improved non-dominated sorting multi-target grey wolf optimization algorithm with adaptive weight adjustment; and a closed-loop control execution unit. According to the invention, photovoltaic, energy storage, load and power grid operation data in the power distribution network are comprehensively captured through the source-network-load-storage full-dimension data acquisition unit, and spatial correlation mapping of topological nodes of the power distribution network in a multi-source heterogeneous data fusion processing process is combined; and constraint conditions such as power balance and node voltage during dynamic multi-objective optimization decision making are included, so that neglect on topology and operation constraints of the power distribution network is effectively made up.

A wind-photovoltaic ratio system optimization configuration method for a new energy hydrogen production park, the method comprising the steps of: S1) calculating an output time series of local wind power and photovoltaic power generation, and analyzing wind power and photovoltaic output characteristics and a wind-photovoltaic correlation; S2) establishing equivalent device models of a hydrogen energy storage unit and an electrochemical energy storage unit to form an electric bus power balance and hydrogen bus power balance relationship, and taking the minimization of a full life cycle cost as an objective function and taking into consideration constraint conditions such as the power balance, device operation characteristics and a land restriction to establish a system planning optimization configuration model for a new energy hydrogen production park, which model takes a wind-photovoltaic ratio into consideration; S3) integrating the objective function and the constraint conditions to obtain a system planning and optimization configuration scheme for the new energy hydrogen production park and the total system configuration cost; and S4) taking into consideration an electricity price factor and an energy storage operation characteristic constraint, performing annual hydrogen production timing operation simulation, and analyzing a park electricity purchase demand and a new energy consumption capacity under the present planning optimization configuration scheme.

The invention discloses an island micro-grid operation scheduling method and device. According to the scheme, the method comprises the steps that power generation characteristic modeling is conducted on a distributed power source and an energy storage system of a diesel storage island micro-grid; constructing a function taking the minimum daily total operation cost as a target, wherein the function covers the operation cost of a diesel generator and an energy storage system and the pollutant treatment cost; establishing a power impact type load mathematical model including power characteristics, system dynamic frequency response characteristics, micro-grid dynamic frequency safety constraints and diesel generator frequency control constraints; establishing a micro-grid operation constraint model which has constraints such as power balance, Distflow power flow, energy storage, charging, discharging and capacity, diesel generator output and climbing rate; and solving the model by using a particle swarm optimization algorithm, generating a diesel generator output, energy storage system charging and discharging and frequency modulation reserve capacity distribution strategy, and obtaining an island microgrid optimal operation scheduling decision.

The invention relates to the technical field of micro-grids, and discloses a micro-grid real-time load balancing scheduling method and system based on deep reinforcement learning, and the system comprises a multi-source sensing module, an intelligent decision module, a safety protection module, an execution control module, a digital twin module and an energy efficiency evaluation module. When real-time load balancing scheduling of the micro-grid is carried out, a multi-time-scale scheduling strategy is generated in real time by dynamically coordinating economical efficiency, environmental protection and power supply reliability targets through a deep reinforcement learning agent, so that the problems of high operation cost, standard exceeding of carbon emission and voltage instability caused by a single optimization target in a traditional method are further solved, and the real-time load balancing scheduling of the micro-grid is realized. According to the method, the multi-dimensional collaborative optimization of the micro-grid in a complex operation environment is ensured, and meanwhile, a cloud global optimization module generates a long-cycle strategy, so that the problems of response delay and insufficient expandability of a centralized control architecture are further solved, and the real-time power balance capability in a high-permeability renewable energy source scene is improved.

The invention provides a photovoltaic output prediction method and a distributed optical storage transformer area active / reactive support capability optimization method, and belongs to the technical field of power system automation. Comprising the following steps: constructing a prediction model based on meteorological information, extracting multi-scale features of meteorological data, reducing dimensions through kernel principal component analysis, predicting photovoltaic power through a long-short-term memory network, quantifying the uncertainty of the photovoltaic power, and forming a prediction interval. Based on a photovoltaic power prediction result, a robust nonlinear programming model is established, the sum of absolute values of active / reactive power transmitted to a main power grid by a light storage area is maximized as a target, and linear and nonlinear constraints such as energy storage operation, power balance, equipment capacity and power factors are considered at the same time. The robustness under the photovoltaic output uncertainty is ensured through a budget uncertainty set; the model is solved by adopting a sequential quadratic programming algorithm. According to the method, the photovoltaic power prediction precision is effectively improved, and organic combination of high-precision prediction and robust nonlinear optimization is realized.

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.

The invention relates to a sodium electric energy storage system dynamic partition power control method based on string type cluster level management, which comprises the following steps of: firstly, realizing physical isolation of battery clusters through a string type framework, configuring an independent power conversion module and a cluster level controller for each cluster, acquiring voltage, current, SOC (state of charge) and SOH (state of health) of each cluster in real time, and on the basis of the data, in a discharging or discharging process, determining the state of charge of each cluster; a dynamic partition strategy and automatic adjustment of power distribution are realized according to inter-cluster SOC, SOH, voltage range, temperature range, power limit and other differences, and an energy storage system is dynamically divided into a high-energy high-health area, a high-energy medium-health area, a medium-energy high-health area, a medium-energy medium-health area, a low-energy or sub-health area and a fault early warning area in a discharging and charging mode; and rapid balance of electric quantity among clusters is realized through the converter module, so that power distribution is ensured to meet power grid requirements, overcharge and overdischarge of batteries are avoided, and the service life of a battery system is prolonged.

The invention relates to the technical field of power systems, and discloses a new energy power control method and device based on distributed power supply access, and the method comprises the steps: constructing a unified topology model comprising distributed source loads, energy storage equipment and charging piles based on an access mode of a distributed power supply; acquiring operation data of the distributed power supply through the unified topology model, and performing data preprocessing on the operation data to generate a standardized operation data set; monitoring the real-time operation state and operation index of the power grid in real time based on the standardized operation data set, dynamically generating a corresponding power control instruction, and issuing the power control instruction to the distributed power supply to execute a power regulation task; and generating a power balance instruction after the occurrence of the power grid abnormal event is detected, decomposing the power balance instruction based on the unified topology model and the standardized operation data set, and executing a power balance task. According to the invention, real-time monitoring and dynamic power adjustment of the distributed power supply can be realized, and the power grid stability and the power balance capability are improved.

The invention relates to the field of locomotive power battery electric quantity control, and particularly discloses a locomotive power battery electric quantity hysteresis equalization control method, which comprises the following steps that: a traction force exerting mode is reset into an axle load transfer mode, and the initial value of an electric quantity equalization compensation coefficient K is a rack axle load transfer coefficient corresponding to zero speed; judging whether the current mode is a power battery power supply mode; if yes, the TCU receives the electric quantity of a front power battery and the electric quantity of a rear power battery in real time according to the current locomotive working mode, calculates the electric quantity average value difference value, monitors the auxiliary load power and the traction motor power and calculates the power difference value; entering a hysteresis control logic process; calculating a dynamic traction force compensation coefficient, ensuring that the traction force compensation amount is matched with the actual load characteristic of the system, correcting the traction characteristic curve in real time, and dynamically compensating the electric quantity difference of the battery; and performing traction distribution control according to the locomotive speed and outputting corresponding traction.

The invention discloses an island energy system opportunity constraint weekly optimization scheduling method and system, and the method comprises the steps: converting the opportunity constraint of the actual output of wind and light into a deterministic inequality constraint based on the prediction error probability distribution characteristics of wind power and photovoltaic; according to the obtained wind and light output constraint, a dynamic operation model is established, and waste heat output of the hydrogen fuel cell is coupled with an absorption refrigerator; with the purpose of minimizing the total operation cost of the system, combining the obtained deterministic inequality constraint and the dynamic operation model, defining the power balance constraint of electricity / heat / cold / fresh water, the hydrogen storage tank capacity constraint and the equipment climbing rate constraint, and constructing an optimization model; and solving the optimization model by adopting mixed integer programming to obtain an optimization scheduling scheme. The requirements of electricity, cold, heat and fresh water of an ocean island energy system are met, and meanwhile energy storage and cross-period utilization during wind and light resource enrichment are achieved.

The invention belongs to the field of power distribution network operation control, and discloses a power distribution network global optimization regulation and control method, system and device and a medium, and the method comprises the steps: obtaining multi-dimensional indexes including regional modularity, regional reactive power balance degree and regional active power balance degree in a power distribution network; based on a comprehensive evaluation mechanism of a multi-dimensional index, an optimal power distribution network region division result is obtained by utilizing iterative calculation of a heuristic search algorithm; a power distribution network global optimization model with cost optimization as a target is constructed so as to realize control of the comprehensive cost of the power distribution network; on the basis of a distributed optimization algorithm, a global optimization problem of the power distribution network is decomposed into a cloud main problem and a side terminal problem, and global optimality of resource allocation among different power distribution network regions is realized by solving the cloud main problem; and the optimal control strategy in the power distribution network area is realized by using the control result of the comprehensive cost of the power distribution network and the solution of the edge terminal problem. And the coordination control of the global massive controllable resources of the power distribution network is realized.

The invention relates to the technical field of battery energy storage station heat management, and discloses a battery energy storage station heat management optimization method based on multi-target control, which comprises the following steps: constructing a mathematical model of an energy storage station heat management system, and defining a state variable and a control variable of the model; the mathematical model comprises a battery heat production model, a battery heat exchange model, an SOC dynamic model and an energy storage station power balance model; based on the mathematical model of the thermal management system of the energy storage station, establishing a multi-target comprehensive evaluation function and setting constraint conditions; according to the defined state variables and control variables, the multi-objective comprehensive evaluation function and set constraint conditions, learning iteration is carried out based on a Q learning algorithm, a Q value is obtained when a preset convergence condition is reached, and an optimal control variable distribution strategy is output; and performing thermal management optimization on the energy storage station based on the optimal control variable distribution strategy. According to the invention, the balance among the targets is realized based on the combination of the multi-target control strategy and the optimization algorithm, and guarantee is provided for the performance and safety of the battery energy storage station.

The invention discloses a construction method and a modulation method of a three-port multi-frequency MMC topology based on bridge arm multiplexing, and relates to the technical field of power electronics. The construction method comprises the steps that sub-modules of a traditional double-bridge-arm MMC are integrated and optimized into a three-bridge-arm structure through a current conversion branch, independent energy interaction of three ports is achieved through an auxiliary bridge arm, multi-frequency operation modes of power frequency, frequency division, direct current and the like are supported at the same time, and therefore the utilization rate of the bridge arm sub-modules is increased, and the energy utilization rate of the MMC is increased. And the overall size and the manufacturing cost of the converter are reduced. Based on three-port multi-frequency coordination control and power balance requirements, a modulation method of the topological structure is provided, a multi-frequency coordination modulation strategy is adopted, frequency offset is performed through an auxiliary bridge arm to reduce cross interference of port frequency, and output waveform quality is improved through multi-objective optimized capacitor voltage balance control. And meanwhile, direct-current side faults are effectively eliminated by means of the auxiliary bridge arm.

The invention discloses a computing power resource dynamic scheduling system integrating environmental perception and power self-balancing, and relates to the technical field of computing power resource scheduling. According to the system, multi-dimensional data inside and outside a specified computing power facility operated by a server cluster are collected in real time through a data collection and environment perception fusion module, and dynamic / static fusion processing is carried out on the multi-dimensional data and current environment perception parameters, so that comprehensive perception of the environment and the operation state is realized; then, resource configuration is optimized by combining a combined scheduling and self-power balance control module with a fusion processing result; finally, a hierarchical scheduling scheme is generated through a hierarchical scheduling and multi-scene adaptation verification module, and whether the generated hierarchical scheduling scheme adapts to the current scene or not is judged by combining progressive derating judgment and error-tolerant rate verification, so that dynamic linkage regulation and control of the load, the environment and the power are realized, the problem of unbalanced power distribution in computing power scheduling is effectively solved, and the service life of the computing power scheduling system is prolonged. And the energy efficiency ratio and the environment adaptive capability of the server cluster are improved.

The invention discloses a cooperation system and method between big data and a virtual power plant, the system comprises a plurality of power generation terminals, an energy storage terminal, a power utilization terminal, an energy management cloud platform, a power grid and a power market, the power generation terminals aggregate small solar energy and wind energy dispersed in different spaces to form a unified scheduling energy supply platform; according to the method, the multi-target optimization problem in the system is effectively solved by using the multi-target particle swarm optimization algorithm, and the targets of power fluctuation minimization and income maximization are achieved. According to the method, big data and a virtual power plant are used for cooperation, scheduling optimization is carried out through an improved multi-objective particle swarm optimization algorithm and an objective function with the maximum electric-interference double-optimal metric value, and under the conditions of meeting power balance constraints, energy storage capacity constraints and the like, the optimal scheduling strategy is searched, electric power fluctuation is balanced, and multiple objectives of income are achieved; and finally, outputting a cooperative scheduling scheme satisfying multi-objective optimization, and realizing power fluctuation minimization and income maximization.

The invention relates to the technical field of distributed micro-grids, and discloses a distributed micro-grid grid-connected protection and power balance intelligent regulation and control method, which comprises the following steps of: firstly, acquiring multi-source operation data of a micro-grid, extracting characteristics by using an intelligent analysis model to obtain an operation characteristic sequence, and calculating a grid correlation coefficient; dimensionality reduction is carried out on correlation matrixes of correlation coefficients of different regional power grids, key feature vectors are extracted, and regulation and control weights of the regional power grids are obtained through Sigmoid function conversion. And dynamically adjusting the reference regulation and control points of the operation feature sequence according to the weights, generating regulation and control parameters and updating regulation and control records. In addition, a multi-dimensional power grid regulation and control space is constructed to monitor abnormity, and a joint optimization model is established to solve an optimal regulation and control scheme. According to the method, the operation state can be accurately analyzed, and the grid-connected protection capability and the power balance regulation and control level of the distributed micro-grid are effectively improved.

The invention discloses a two-stage optimization method for carbon constraint micro-grid energy management. The method comprises the following steps: establishing power balance constraints of various components in a micro-grid, an economic-low carbon joint objective function, optimization solution of diesel engine power, grid transaction, a load strategy and energy storage scheduling; the system-level CVaR tail risk index is decoupled to a short-time-period CVaR value according to the probability distribution of the system-level CVaR tail risk index and the time window area proportion, and the short-time-period CVaR value serves as a carbon over-quota emission risk constraint boundary and is transmitted to the intra-day level; actually measured wind and light load data are fed back to the prediction model for real-time correction in each period; constructing a robust optimization model to dynamically adjust conservative coefficients; the risk budget is used as a hard constraint, and the expected value of the excess carbon emission condition in the window is calculated in real time; a closed loop is formed. The intra-day rolling optimization provided by the invention is based on an ultra-short-term prediction technology, and the prediction error of the intra-day rolling optimization is only slowly increased in a finite time window, so that the prediction precision at the same time point is obviously superior to that of a day-ahead optimization result.

The invention provides a photovoltaic consumption-oriented electric vehicle battery swap station collaborative planning method, and the method comprises the steps: obtaining planning input data which comprises power distribution network operation parameters, user charging demand data, electric vehicle battery swap station battery data, electricity price subsidy data and photovoltaic power generation data; planning constraint conditions are determined, wherein the planning constraint conditions comprise an electric vehicle battery swap station operation constraint, a node power balance constraint, a transformer substation output constraint, a line power flow limit value constraint, a node voltage constraint and a photovoltaic consumption constraint; determining a target function, wherein the target function is used for cooperatively optimizing the construction position and energy storage configuration of the battery swap station, a charging subsidy gear trigger strategy and a photovoltaic consumption level; and according to the planning input data, the planning constraint condition and the objective function, establishing and solving a mixed integer linear programming model to obtain an electric vehicle battery swap station collaborative programming scheme. According to the obtained electric vehicle battery swap station collaborative planning scheme, the consumption level of local photovoltaic electric energy is effectively improved.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention belongs to the technical field of power grid systems, and particularly relates to a cooperative scheduling method for a water-electricity-heat combined supply negative carbon building light-storage direct-flexible energy microgrid group. Comprising the following steps: constructing a negative carbon building light storage direct-flexible energy microgrid group mathematical model; wherein the mathematical model comprises a generator constraint model, an external power grid and micro-grid group exchange power constraint model, a power balance constraint model, a peak-valley difference constraint model, a storage battery constraint model, a phase change heat storage system operation constraint model and a water supply system operation constraint model; constructing a target function of the scheduling model by taking the minimum total operation cost of the negative carbon building light storage direct-flexible energy microgrid group in a scheduling period as a target; wherein the total operation cost comprises electric power procurement cost, carbon emission cost, energy storage cost, fuel cost and peak-valley difference punishment cost. Through linkage of the electricity storage system, the heat storage system and the water storage system, the utilization rate of renewable energy sources and the flexibility of the system are maximized.

The invention relates to the technical field of bidirectional PCS, and discloses a bidirectional PCS converter multi-target power balance optimization method and system based on model predictive control. According to the method, real-time operation parameters are analyzed by using a first-order lag initial model, prediction of power disturbance in a plurality of control periods in the future is realized, power balance optimization is converted from passive response to active pre-judgment, and a linear time-varying prediction model is constructed, so that the real-time performance of power balance optimization is improved. According to the method, the evolution trajectory of the direct current bus voltage in a period of time in the future is predicted, the power balance optimization behavior is continuous and prospective through a rolling optimization mechanism of prediction, evaluation and preferential selection, and meanwhile, the three targets of ensuring voltage stability, reducing switching loss and suppressing current harmonic content are fused into power balance optimization, so that the power balance optimization efficiency is improved. The problems of control hysteresis and target singleness existing in a traditional control method are solved, and quick and accurate power balance control of the bidirectional PCS is realized.

A space-time learning and lightweight direct-current optimal power flow projection scheduling method driven by a reactance perception graph kernel comprises the steps that the reactance perception graph kernel based on a susceptance matrix is constructed, a power grid node reactance perception spatial dependency relationship is extracted, and multi-period node feature tensors are organized; a space-time learning device comprising a graph convolution encoder and a time sequence encoder is adopted to carry out space-time joint modeling on the node features, and the cross-time evolution law of the node marginal electricity price and the voltage phase angle is learned; mechanism consistent physical regular terms of load flow safety and climbing consistency are introduced in training, and physical constraint influences are gradually enhanced by means of weight scheduling; and converting the phase angle trajectory into an equivalent unit output initial value through physical mapping, constructing a lightweight quadratic programming projection model with the minimum deviation initial value, and generating a feasible day-ahead scheduling plan under the constraint of meeting power balance and the like. According to the method, the whole link from prediction to scheduling is opened, the speed and the reliability are both considered, and an engineering available path is provided for day-ahead multi-period clearing and large-scale scene analysis.

The invention aims to provide a dynamic energy optimization scheduling method and system for a temporarily-built optical storage system, and belongs to the technical field of temporarily-built optical storage system control, and the method comprises the steps: obtaining the photovoltaic power generation power, the energy storage charge state and the load demand in real time, judging the power balance state, and controlling the cooperative operation of each unit according to the power balance state. When photovoltaic power generation is sufficient, redundant electric energy is stored in the energy storage unit; if not enough, energy storage and electricity supplementation are preferentially carried out, and the diesel generator is started if not enough. The system also combines historical data and a neural network to predict a future scheduling strategy, and optimizes an energy storage charging and discharging plan. In addition, non-critical loads are identified through the intelligent electric meter, and the power supply priority is dynamically adjusted. The system improves the operation efficiency and stability of the optical storage system, prolongs the service life of equipment, and is suitable for temporary building facility power supply scenes.

The invention discloses a day-ahead market scheduling optimization method for power balance characteristics in an embedded scheduling time period, and the method comprises the following steps: 1), constructing an actual output expression and a net load prediction power expression of a generator set based on an asymmetric affine strategy; 2) based on the opportunity constraint optimal power flow, constructing a day-ahead market economic dispatching model of power balance characteristics and uncertainty in an embedded dispatching time period; 3) analyzing and converting the day-ahead market economic dispatching model with the power balance characteristics and uncertainty in the embedded dispatching time period, and constructing a day-ahead market safety constraint economic dispatching model with the power balance characteristics in the embedded interval; and 4) solving the day-ahead market security constraint economic dispatching model embedded with the power balance characteristics in the interval to obtain a node marginal price, and outputting a market dispatching scheme with the minimum node marginal price. According to the method, on the basis of considering the uncertainty of the net load prediction error, the power transmission risk cost caused by the uncertainty can be effectively internally reduced.