117 results about "Economic scheduling" patented technology

The invention discloses a power distribution network-virtual power plant non-iterative collaborative optimization scheduling method and a related device, and relates to the field of power system optimization scheduling calculation, and the method comprises the steps: building a virtual power plant optimal economic scheduling model according to the characteristics of access resources in a virtual power plant; according to the optimal economic dispatching model of the virtual power plant, determining a variation range constraint of the power of the tie line of the virtual power plant based on a rapid approximate projection method of a feasible region of operation of the virtual power plant; constructing a non-iterative collaborative optimization scheduling model of the power distribution network and the virtual power plant based on variation range constraints according to a scheduling decision target of the power distribution network, a scheduling target of the virtual power plant and characteristics of access resources; according to the collaborative optimization scheduling model, solving a power distribution network scheduling result and a virtual power plant tie line power scheduling result; and inputting the tie line power scheduling result into the optimal economic scheduling model of the virtual power plant to obtain an optimal scheduling result of internal resources of the virtual power plant. According to the invention, the decision-making efficiency of collaborative optimization scheduling can be improved.

The invention relates to the technical field of electric power system regulation and control, and particularly discloses an electric power active balance control method for coupling multi-element energy storage of a coal power unit, which is used for solving the problem that the existing static active safety margin evaluation method cannot evaluate the safety of the coal power unit under the complex working conditions of high wind power penetration, large disturbance, system inertia decline and the like. The method solves the problems that multi-stage frequency response cannot be accurately described, online self-adaptive power distribution and continuous smooth scheduling are lacked, and consequently frequency drop is large, recovery is slow and output suddenly changes, and comprises the steps of building a collaborative characteristic model, calculating dominant characteristics on line, optimizing scheduling in a segmented mode and updating parameters in a closed loop. According to the method, by constructing a hierarchical inertia and multi-stage frequency modulation collaborative model, and adopting multi-scale online feature extraction and adaptive power distribution, segmented gradient optimization scheduling and closed-loop online parameter updating, quick response, smooth connection and accurate optimization of frequency support and economic scheduling in a wind power high-permeability scene are realized.

The invention provides a new energy power grid prospective scheduling method and device, and relates to the technical field of electric power system intraday economic scheduling. The method comprises the following steps: firstly, constructing an opportunity constraint optimal power flow model of prospective scheduling, analyzing the influence of new energy uncertainty on opportunity constraint, establishing a constrained Markov decision process of prospective scheduling, and then utilizing a risk evaluator network fitting risk function probability distribution and an actuator network considering extreme scene performance to determine the opportunity constraint optimal power flow model of prospective scheduling. The processing capability of the intelligent agent on a prospective scheduling scene containing a new energy extreme climbing event is enhanced; and finally, the training of the intelligent agent is accelerated by utilizing an imitation learning technology in a power grid prospective scheduling off-line simulation environment. According to the method, the solving speed and the strategy robustness and safety of the double-layer robust optimization model of the look-ahead scheduling can be considered.

The invention discloses a multi-park power-carbon cooperative scheduling strategy considering power generation side life cycle carbon emission. The method comprises the following steps: acquiring related data information in a multi-industrial park system, and constructing a life cycle carbon emission accounting model of a thermal power generation system and a photovoltaic power generation system; a carbon compensation mechanism is provided, the carbon emission reduction characteristic of the photovoltaic power generation system is used for counteracting the life cycle carbon emission of the photovoltaic power generation system in the production, construction and decommissioning stages, and the interior of the life cycle carbon emission responsibility of the photovoltaic power generation system is promoted; an upper-layer model is mainly combined with a carbon compensation mechanism to solve a scheduling strategy of a power distribution network and calculate node carbon intensity, and a lower-layer model is used for calculating node carbon intensity on the basis of dynamic carbon emission factors calculated by the upper-layer model. And performing low-carbon economic dispatching by taking the lowest operating cost and carbon trading cost of the park as a target, solving an optimal production and power-carbon trading strategy of the industrial park through double-layer model iteration, and processing an oscillation phenomenon by adopting a heuristic method based on a bisection method.

The invention discloses a virtual power plant robust optimization scheduling method based on source load uncertainty, and belongs to the technical field of energy management. The method comprises the following steps: step 1, power supply side uncertainty modeling; step 2, load uncertainty modeling is carried out; 3, carrying out two-stage random robust optimization scheduling modeling; and step 4, solving the two-stage random robust optimization scheduling model. The method has the following beneficial effects: (1) according to the two-stage random robust optimization scheduling method, the economic scheduling of each unit meeting the multi-energy load is realized; and (2) according to a two-stage random robust optimization scheduling strategy, a scheduling scheme with the minimum cost is obtained in the worst scene. And (3) economic dispatching strategies are made for various uncertain conditions by adjusting robust optimization parameters, and the environmental adaptability of the virtual power plant is improved. And (4) in the energy market balance power deviation of the virtual power plant, the economical efficiency is more remarkable.

The invention discloses a virtual power plant collaborative optimization scheduling method and system based on deep reinforcement learning, and the method comprises the steps: building a multi-time scale dynamic model covering second-level frequency response and hour-level economic scheduling through collecting the operation data of a virtual power plant, constructing a mixed sensing network, and taking the multi-time scale dynamic model as the input, extracting multi-scale time sequence features by combining a multi-head hierarchical attention encoder, obtaining equipment dynamic association features through a dynamic heterogeneous graph neural network, and generating comprehensive feature representation; constructing a hierarchical deep reinforcement learning control framework based on comprehensive feature representation, optimizing energy storage charging and discharging power, a load regulation instruction and a unit output strategy by adopting a deep reinforcement learning algorithm based on an experience playback mechanism, and constructing a multi-target reward function; and finally performing training-deployment process and simulation verification. According to the method, the response time of a protection mechanism can be shortened to be within 0.1 second under the working condition that the SOC is lower than 20%, the comprehensive operation cost of the system can be reduced under the normal working condition, and the signal-to-noise ratio of input signals of a control layer is remarkably increased.

The invention relates to the technical field of power system resource optimization scheduling, in particular to a deep scheduling method and system for deep peak regulation of a coal-fired unit, and the method comprises the steps: collecting multi-dimensional operation parameters of the unit, a deep peak regulation instruction and a carbon price signal of a carbon emission permit trading market, and achieving timestamp alignment; constructing a five-dimensional security constraint model associated with the economic cost boundary; taking peak regulation income maximization as a target, combining fire coal purchase cost, carbon transaction cost and equipment maintenance cost to construct a target function, adopting a dynamic planning algorithm to solve an economic dispatching scheme, and executing a cross-system cooperation strategy; and on the basis of LSTM-based equipment life prediction and digital twin simulation, triggering a cross-system correction strategy. According to the method, the commercial cooperation problem of economy, environmental protection and equipment reliability in deep peak regulation is solved, and the peak regulation income maximization of the coal-fired unit in an electricity market environment is realized.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention relates to a transformer area micro energy network multi-time scale optimization scheduling method and device, and the method comprises the steps: considering the uncertainty of a renewable distributed power supply and a load in a transformer area, and building a micro energy network economic scheduling model based on two-stage robust optimization; the method comprises the following steps: converting a micro-energy grid economic dispatching model based on two-stage robust optimization into a main problem and a sub-problem in a mixed integer linear form, solving the main problem and the sub-problem to obtain an optimal decision variable under the worst condition, and taking the optimal decision variable as a day-ahead plan value; establishing an intra-day tie line power tracking model by taking the minimum error between the station area tie line fluctuation and the storage battery charge state estimated output value and the day-ahead planned value as a target; solving the intra-day tie line power tracking model to obtain an intra-day real-time scheduling strategy; and adjusting the intra-day real-time scheduling strategy by using a model prediction control algorithm. According to the method, the problems of uncertainty and volatility of the source load can be solved.

The invention provides a virtual power plant optimal scheduling method based on a whale algorithm. The method comprises the steps that an electric heating comprehensive system economic scheduling target with the lowest comprehensive operation cost as the target is determined; constructing constraint conditions of the virtual power plant optimization scheduling model; the whale algorithm is used to solve the economic dispatch target; and analyzing a model result, and evaluating the operation benefit of the virtual power plant. In order to solve the problems that virtual power plant optimization scheduling is high in calculation complexity, low in convergence speed and prone to falling into a local optimal solution, the whale algorithm scheme is adopted, the convergence speed and precision of the algorithm are improved through natural predation behaviors simulated by the whale algorithm and optimization of contraction factors and expansion factors, the calculation time is effectively shortened, and the optimal scheduling efficiency is improved. And the adaptability and the flexibility of the algorithm are enhanced through the shrinkage factor and the expansion factor which can be dynamically adjusted, so that the effects of optimizing the operation cost of the power system, improving the resource configuration efficiency and enhancing the stability of the power grid are achieved, and the development of sustainable energy is promoted.

The invention relates to an active power distribution network cloud-side collaborative day-ahead scheduling method, device, equipment and medium, and the method comprises the steps: building an ADN distributed day-ahead economic scheduling model with the optimal daily operation cost of an active power distribution network as a target under the condition of considering the carbon emission and the interaction cost of a main power grid; under the constraint condition of the ADN distributed day-ahead economic dispatching model, establishing a multi-agent distribution deep reinforcement learning model based on an MADDPG algorithm; the multi-agent distribution deep reinforcement learning model is trained in the cloud server, and the trained multi-agent distribution deep reinforcement learning model is sent to each agent edge server; and in each control period, each agent edge server adopts the received trained multi-agent distribution deep reinforcement learning model to perform active power distribution network dispatching control of the sub-region according to real-time local observation information. The method is suitable for an ADN operation scene with small edge calculation amount and low communication requirement.

The invention discloses an industrial micro-grid group double-layer optimization scheduling method for electricity-hydrogen shared energy storage, and aims to make full use of energy consumption complementation characteristics of different parks. A refinement model of various devices, electricity-hydrogen shared energy storage components and the like in the polycrystalline silicon micro-grid is constructed by considering a hydrogen recovery system. Based on a Stackelberg game theory, a double-layer optimization scheduling model with shared energy storage as a leader and a plurality of polycrystalline silicon micro-grids as followers is constructed. The upper layer model is an EHSES multi-target optimization scheduling model considering economy, environmental protection and sustainability, the lower layer model is an economic scheduling model of each polycrystalline silicon microgrid, and iterative optimization of the electro-hydrogen price of the EHSES and the polycrystalline silicon microgrid is realized. And solving the model by using a multi-target myxomycete algorithm based on reverse learning and a reference point and a CPLEX solver. Simulation results show that the model can effectively improve the economy, environmental protection and sustainability of the polycrystalline silicon micro-grid cluster, and the effectiveness of the model is verified.

The invention relates to the technical field of power systems, in particular to a comprehensive energy optimization scheduling system and method considering source-load coordination. An economic dispatching mathematical model is established by taking variables such as fire coal cost of a thermal power generating unit, operation cost of a steam extraction and energy storage device, system operation and maintenance cost, solution loss cost in carbon capture equipment, system wind and light abandoning cost and stepped carbon transaction cost as basic objective functions through an operation decision framework taking a comprehensive energy system as a dispatching basis; basic constraint conditions such as a power balance constraint, a spinning reserve constraint, a wind and light output constraint, a carbon capture device operation constraint and an energy storage constraint are comprehensively considered, and an improved single-target artificial bee colony algorithm is utilized to solve the economic dispatching mathematical model to obtain an optimal solution; therefore, an optimal balance point is found among economy, sustainability and system operation requirements.

The invention discloses a virtual power plant distributed collaborative optimization method and system for communication interruption, and the method comprises the steps: building a scheduling control model of a virtual power plant based on a communication network topology structure of distributed power generation nodes in a power grid system, and generating a cost optimization function according to the active output power of each distributed power generation node, generating an initial virtual reference signal based on the cost optimization function, smoothing the initial virtual reference signal, generating a smooth reference signal, generating a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal, and optimizing the scheduling control model based on the decentralized controller. According to the method, the distributed cooperative scheduling of the virtual power plant in a communication interruption scene is optimized, so that the dependence on continuous communication is effectively reduced while the economic scheduling precision is guaranteed, the scheduling continuity and the control response capability of the virtual power plant are improved, and the economic efficiency of the virtual power plant is improved. Coordinated control and stable operation of the virtual power plant under an unstable communication condition are effectively guaranteed.

The invention discloses a multi-micro-grid power distribution system two-stage robust collaborative optimization method and device, and belongs to the technical field of multi-micro-grid power distribution system optimization scheduling, and the method comprises the steps: inputting the obtained basic operation data and uncertainty characterization parameters of a multi-micro-grid power distribution system into a pre-constructed multi-micro-grid economic scheduling model, solving the model by adopting a column and constraint generation algorithm to obtain a day-ahead scheduling plan of the multi-microgrid power distribution system in the worst scene considering the space-time correlation; inputting the day-ahead scheduling plan, the obtained basic operation data of the multi-microgrid power distribution system and the tie line power coupling constraint between the power distribution network and the multiple microgrids into a pre-constructed double-layer collaborative optimization model, and solving the model in a distributed manner by adopting an alternating direction multiplier method, an intra-day optimization scheduling scheme meeting the requirement of good cooperative operation of the power distribution network and the multi-micro network friends is obtained; according to the method, the optimization result in the worst scene meets the robustness requirement, and meanwhile, the economical efficiency of the system is guaranteed.

The invention relates to the technical field of power systems and energy management, discloses an industrial and commercial energy storage system energy resource scheduling method and system, and aims to solve the problems that in the prior art, economy optimization and battery health management are separated, degradation model simplification causes life damage, and the adaptability to load and electricity price uncertainty is insufficient. Specifically, multi-source heterogeneous state data are fused; constructing and calibrating a battery digital twinborn model coupled with electrochemistry, a thermal field and an aging mechanism; and executing a three-layer cooperative strategy of long-term degradation track planning, medium-term random scheduling optimization and short-term model prediction control, enabling the battery degradation cost to be generated in an economic scheduling target, and achieving high-robustness real-time control through rolling optimization. The system comprises a data acquisition and fusion unit, a digital twinning module, a probability prediction module, a hierarchical scheduling engine and an instruction execution unit. According to the invention, the unification of full-life-cycle value maximization and operation reliability is realized.

The invention discloses a power grid optimization scheduling method and system based on an intelligent optimization algorithm, and relates to the technical field of intelligent power grids, and the method comprises the steps: constructing an intelligent power grid energy model, and determining a total income value set of a user; acquiring a dual variable set, a transaction energy mismatching limit value set and an energy transmission space variable set of the user; performing embedded coding on a total revenue value set, a dual variable set, a transaction energy mismatching limit value set and an energy transmission space variable set of the user by using the embedded matrix; obtaining an economic allocation quota multi-mode collaborative self-interaction representation vector; based on the economic allocation quota multi-modal collaborative self-interaction representation vector, generating an economic allocation quota value of the user; and scheduling based on the economic allocation quota value of the user to complete power grid optimization scheduling. The calculation accuracy and rationality of the economic dispatching quota value are effectively improved, and more accurate economic dispatching quota reference is provided for users.

The invention relates to the field of power system scheduling, in particular to a novel power system robust optimization scheduling method based on node carbon potential response. Aiming at the defects that in the prior art, carbon emission responsibility allocation is unreasonable, a demand response mechanism is imperfect, and a scheduling model comprehensively considering economical efficiency and low-carbon performance is lacked, a carbon emission flow model is constructed, node carbon potential is calculated, and responsibility is allocated; a carbon emission responsibility allocation mechanism is established, load node carbon emission is subdivided and calculated, and a quota is determined; developing a dual low-carbon demand response model which comprises a price type model and an excitation type model; and constructing a source-load collaborative low-carbon optimal scheduling model which comprises an upper-layer economic scheduling model and a lower-layer low-carbon scheduling model. Through cooperative work and iterative optimization of the models, fairness allocation of carbon emission responsibility, improvement of demand response effectiveness, dual optimization of economy and low-carbon performance of the electric power system and improvement of stability are realized, and the method is suitable for various energy power generation and electric power systems with different types of loads.

The invention discloses a power system dynamic economic dispatching decision-making method and system oriented to reliability improvement. The method comprises the following steps: constructing a Markov decision-making process of dynamic economic dispatching; constructing a reinforcement learning environment; updating the neural network of distributed reinforcement learning; obtaining feasible power output under the constraint condition; obtaining the total power generation cost through an average consensus algorithm; a greedy strategy epsilon-greedy algorithm is introduced, and the intelligent agent is utilized to maximize a reward which can be obtained currently, so that the intelligent agent is ensured to learn an optimal strategy in a complex environment; and defining # imgabs0 # as a historical optimal strategy, and updating a local operation strategy, so that the optimal scheduling strategy is monotonically decreased, and finally, the optimal scheduling strategy is achieved. The method has high robustness under the condition of fluctuating power demand.

The invention relates to the technical field of power systems, and particularly discloses a multi-park integrated energy system scheduling method based on personalized federal reinforcement learning, and the method comprises the steps: building a multi-park integrated energy system low-carbon economic scheduling model with the minimum daily operation cost as the target, the constraint conditions comprise park equipment power output upper and lower limit constraints, storage battery operation constraints, energy supply equality constraints and energy supply inequality constraints; solving the low-carbon economic dispatching model of the multi-park integrated energy system by adopting a personalized federal reinforcement learning method; and solving to obtain a scheduling scheme. The method has the advantages that the efficient low-carbon collaborative scheduling of the multi-park integrated energy system is realized on the premise of strictly protecting the data privacy of each park through a personalized federal reinforcement learning framework.

The present invention discloses a multi-region decomposition coordinated optimization scheduling method, device and storage medium. The method comprises: performing augmented Lagrangian relaxation on the coupling relationship between each adjacent sub-region according to the constraint conditions and objective function of each sub-region to obtain the first augmented Lagrangian function of each sub-region; and using the AMMD algorithm to calculate the first tie-line flow of each sub-region according to the first augmented Lagrangian function; wherein each sub-region is decomposed by the overall regional power system; substituting the tie-line flow of each sub-region as an initial value into the coupling relationship of each corresponding adjacent sub-region to solve the unit combination of each sub-region; and using the ADMM algorithm to calculate the second tie-line flow of each sub-region according to the tie-line flow and unit combination of each sub-region; and performing power system economic scheduling on the overall regional power system according to the unit combination and the second tie-line flow of each sub-region.

The application discloses a power distribution network scheduling method and system, and a computer program product, and belongs to the technical field of smart grids, and comprises the following steps: dividing a power distribution network into multiple power distribution network levels according to voltage grades, collecting data of each power distribution network level, setting a minimum total cost of virtual power plant output as a target function, and constructing an optimization model; starting from a lower power distribution network level, sequentially and independently calculating power flow of each power distribution network level, equivalently taking first-end injected power of the lower power distribution network level as corresponding node load of an upper level, outputting power flow operation conditions of each power distribution network level, establishing a power flow checking constraint model, judging whether the safety operation checking constraint is met, if yes, solving the optimization model by using an optimization algorithm to obtain the minimum total cost and corresponding output values of an output unit group, and if not, reassigning data to perform a new round of optimization. The application considers inverse-level dynamic coupling modeling of grid connection of new power generation units, and provides an integrated solution for dynamic safety and economic scheduling of the power distribution network.

The present invention belongs to the field of smart grid energy-saving and environmental protection economic dispatch, and specifically relates to a distributed economic dispatch method for smart grid based on dynamic event triggering. The method comprises: first, designing a finite time consistency economic dispatch algorithm based on multi-agent consistency, and designing dynamic event triggering conditions according to the finite time consistency economic dispatch algorithm. Secondly, when the dynamic event triggering conditions are met, the finite time consistency economic dispatch algorithm is used to update the incremental cost of the generator and calculate the output power. Finally, an auxiliary variable is introduced, and the finite time consistency economic dispatch algorithm is used to update the auxiliary variable, and the incremental cost is updated with the auxiliary variable, and the output power is calculated so that the output power meets the generator power output power limit. The present invention can perform economic dispatch on the smart grid within a limited time and save communication resources.

The application provides a kind of multi-microgrid collaborative economic dispatching method based on blockchain, comprising: based on microgrid group multi-group architecture model, construct microgrid group dispatching architecture model based on blockchain;Based on objective function submodel, constraint condition submodel and incremental cost submodel, an economic dispatching model is constructed;Wherein, economic dispatching model includes: microgrid layer economic dispatching model and microgrid group layer economic dispatching model;Based on microgrid group dispatching architecture model, intra-group scheduling is carried out on microgrid layer economic dispatching model, and inter-group scheduling is carried out on microgrid group layer economic dispatching model, to obtain multi-microgrid collaborative economic dispatching result.The application constructs microgrid group system distributed dispatching architecture, designs system economic dispatching model with comprehensive economic and environmental protection targets, and proposes a kind of intra-group optimization and inter-group optimization economic dispatching method based on blockchain.In the case of ensuring good expansibility, the economic efficiency and fault tolerance of microgrid group are improved.

The invention discloses a micro-grid economic dispatching method considering energy storage cycle life. The method comprises the following steps: reading local electrical data; calculating the energy storage discharge depth and linearizing the function to obtain the energy storage life loss cost of different discharge depths of energy storage; establishing a micro-grid economic dispatching model taking the minimum daily operation cost of the micro-grid as a target, wherein the daily operation cost of the micro-grid comprises energy storage life loss cost, electricity purchasing and selling cost, operation and maintenance cost of each unit and fuel cost; and solving the micro-grid economic dispatching model, and formulating an energy storage charging and discharging strategy and an electricity purchasing strategy for the micro-grid according to the obtained model decision variable. According to the micro-grid operation scheduling model established by the invention, a new energy unit, a steam turbine unit and energy storage are considered, and the economic scheduling benefit of the micro-grid can be improved through good multi-energy complementation.

The invention relates to the technical field of power dispatching, and provides a digital twin power energy dispatching system based on distributed control, and the system comprises a data collection layer which is used for collecting the operation data of a plurality of energy nodes distributed in a target power grid in real time and the environment data of the environment where each energy node is located; the digital twinborn layer is used for updating a pre-constructed digital twinborn model based on each operation data and each environment data so as to enable the updated digital twinborn model to be mapped with each energy node in real time, and performing analogue simulation and state prediction of power grid operation based on the digital twinborn model to generate simulation prediction data; and the distributed scheduling layer is used for calculating a scheduling instruction set by adopting a distributed optimization algorithm based on the simulation prediction data so as to perform power energy scheduling on the target power grid based on the scheduling instruction set. Safe and economic dispatching of the power grid is achieved, and the intelligent level and the operation economical efficiency of the power grid are remarkably improved.

The present application relates to the technical field of power system resource optimization scheduling, and particularly relates to a deep scheduling method and system for deep peak regulation of coal-fired units, which comprises the following steps: collecting multi-dimensional operation parameters of the unit, deep peak regulation instructions and carbon price signals of the carbon emission trading market, and realizing timestamp alignment; constructing a five-dimensional safety constraint model associated with the economic cost boundary; taking the maximization of peak regulation income as the target, constructing an objective function by fusing the coal purchase cost, carbon trading cost and equipment maintenance cost, solving the economic scheduling scheme by using a dynamic programming algorithm, and executing a cross-system collaborative strategy; based on the LSTM, predicting the equipment life and digital twin simulation, and triggering the cross-system correction strategy. The present application solves the business collaboration problem of economy, environmental protection and equipment reliability in deep peak regulation, and realizes the maximization of peak regulation income of coal-fired units under the power market environment.

The application provides a low-carbon optimization scheduling method for an electric carbon system considering device variable working condition characteristics, relates to the field of low-carbon optimization scheduling of comprehensive energy systems, and comprises the following steps: a variable working condition efficiency model of an energy conversion device is established, linearization processing is performed on the variable working condition efficiency model, and the linearization processing result is mapped to a carbon emission state to construct an improved carbon emission flow model; a carbon emission flow model of an energy storage device is established; the carbon emission flow model of the energy storage device contains a carbon charging rate used for representing a carbon storage state of the energy storage device; based on the improved carbon emission flow model and the carbon emission flow model of the energy storage device, a comprehensive energy optimization scheduling model under multi-market coupling is constructed; and based on the comprehensive energy optimization scheduling model, a distributed robust optimization method is adopted to determine a scheduling scheme of the comprehensive energy system with the minimum total system cost as an optimization target, so that the collaborative optimization of accurate carbon emission measurement and low-carbon economic scheduling can be realized under the condition of uncertain wind and light source load.

The invention provides a wind-light-storage integrated transformer network scheduling method and system, and belongs to the field of transformer distributed scheduling, and the method comprises the steps: designing a distributed optimization strategy, and guaranteeing the minimum operation cost of a transformer network in a normal operation state. Firstly, an economic dispatching problem of a transformer network is modeled into a class of distributed optimization problems; secondly, aiming at the optimization problem, constructing a class of Lagrange functions, and establishing a first-order optimal condition of an optimal value point based on a convex optimization theory; and finally, on the basis of the obtained first-order optimal condition, a class of continuous time distributed primal dual strategies are designed in combination with a primal dual strategy and a consistency strategy. When the strategy is operated, each transformer makes a decision only by using local information. Therefore, the strategy can protect the privacy of the transformer network, reduce the network communication cost, improve the robustness of the network, and ensure the efficient operation of the network.

The invention discloses a micro-grid distributed economic dispatching method based on event triggering fixed time consistency, and belongs to the field of energy-saving and environment-friendly economic dispatching of smart grids. The method is realized based on a multi-agent consistency theory, and specifically comprises the following steps: designing a fixed time consistency distributed economic dispatching algorithm based on the multi-agent consistency theory, designing an event triggering condition according to the algorithm, and updating the communication increment cost of a generator by using the algorithm and the event triggering condition to achieve consistency; and calculating the output power of the generator, and calculating the communication increment cost of the generator by introducing an auxiliary variable, thereby obtaining the output power of the generator, and ensuring that the output power is within the output power limit range of the generator. According to the method, fixed-time economic dispatching of the micro-grid can be realized under the condition that the generator has output power limitation, the stability and the convergence speed of the micro-grid system are improved, unnecessary communication is reduced, and network resources are saved.