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CN-122026512-A - Multi-energy-flow optical storage and filling system cooperative scheduling and optimizing control method

CN122026512ACN 122026512 ACN122026512 ACN 122026512ACN-122026512-A

Abstract

The invention relates to a collaborative scheduling and optimizing control method for a multi-energy-flow optical storage and charging system, and belongs to the technical field of comprehensive energy system optimizing scheduling. The method solves the technical problem that in the prior art, the economy of the optical storage and charging system is difficult to cooperatively optimize due to the intermittence of the photovoltaic output and the fluctuation of the charging load of the electric automobile. The method comprises the steps of carrying out time scale unification and short-term prediction processing on time-sharing electricity price, charging load and photovoltaic output data, constructing a six-way energy flow model among charging loads of a photovoltaic energy storage power grid, taking an energy storage charge state as a decision variable, constructing a multi-objective optimization model with maximized system operation income, minimized power grid load peak-valley difference and minimized energy storage loss rate, solving a pareto optimal solution set by adopting an improved non-dominant sorting genetic algorithm, and realizing rolling optimization scheduling by combining a model prediction control framework. The invention improves the economy of the system, smoothes the load of the power grid, reduces the energy storage loss and enhances the adaptability to uncertainty.

Inventors

  • ZOU MI
  • XIAO YUHAN

Assignees

  • 重庆邮电大学

Dates

Publication Date
20260512
Application Date
20260113

Claims (5)

  1. 1. A multi-energy-flow optical storage and filling system cooperative scheduling and optimizing control method is characterized by comprising the following steps: S1, carrying out unified time scale processing on the time-sharing electricity price, the charging service price of the electric automobile, the charging load of the electric automobile and the photovoltaic power generation output data, and converting multi-source data into a continuous hour-level time sequence; S2, constructing six energy flow models of a photovoltaic-energy storage system, an energy storage-power grid, an energy storage-charging station, an energy storage-power grid and an energy storage-charging station by taking the photovoltaic system, the energy storage system, a power grid and a charging load as research objects; S3, constructing a multi-objective optimization model of the optical storage and charging system on the basis of an energy flow model, wherein the optimization objectives comprise maximization of system operation income, minimization of power grid load peak-valley difference and minimization of energy storage system loss rate; and S4, under the framework of model predictive control MPC, only executing a first time period scheduling strategy on an optimization result in each prediction time domain, updating the system state and the prediction data in real time, and circularly executing the steps to realize rolling optimization scheduling of the optical storage and filling system.
  2. 2. The multi-energy-flow optical storage charging system collaborative scheduling and optimizing control method is characterized in that S1 comprises the steps of uniformly processing time scales of charging load data and photovoltaic power generation output data of an electric vehicle, collecting historical charging load data and photovoltaic power generation output data of the electric vehicle, resampling original data according to uniform time references to a time sequence taking hours as scheduling intervals, filling missing time period data in a zero filling mode, conducting short-term prediction of the charging load of the electric vehicle in future scheduling periods in a prediction time domain based on the charging demand change characteristics of the electric vehicle in the same kind of date and the same kind of time period, conducting photovoltaic power generation output prediction, and conducting prediction of photovoltaic power generation power in the scheduling periods in the prediction time domain based on the historical photovoltaic power generation output data in combination with photovoltaic power output change rules.
  3. 3. The method for collaborative scheduling and optimizing control of a multi-energy-flow optical storage and inflation system according to claim 1, wherein in S2, the energy flow model is on a discrete time scale The following definition, time interval 1 hour, each time period t in the modulation period {1, 2..The, T } establishes a six-way energy flow vector: Wherein the method comprises the steps of Represents the energy of the photovoltaic charge to the stored energy, Represents the energy of the photovoltaic internet access, Representing the discharge of energy from the stored energy to the grid, Indicating that the stored energy is supplying energy to the charging station, Representing the charging of the stored energy by the grid, Representing that the power grid supplies energy to the charging station, and each energy flow satisfies a non-negative constraint; Energy flow distribution to store energy in energy storage system For core mapping variables, energy storage system state transitions satisfy energy balance constraints: Wherein the method comprises the steps of In order to achieve the energy storage and charging efficiency, To the energy storage discharge efficiency and establish energy balance mapping constraint Wherein A is an energy flow relation matrix, Is a constraint vector.
  4. 4. The method for collaborative scheduling and optimizing control of a multi-energy-flow optical storage and inflation system according to claim 1, wherein in S3, the multi-objective optimization model generates benefits in system operation Peak-valley difference of power grid load Loss rate of energy storage system Is an objective function; wherein system operation benefit maximization goal Expressed as: The electric car is charged with electricity price, The electricity purchase price of the power grid is achieved, For the photovoltaic internet electricity price, Cost for running loss; Grid load peak-valley difference minimization target Expressed as: Wherein the method comprises the steps of For an equivalent grid load, ; Energy storage system loss rate minimization goal Expressed as: The improved NSGA-III algorithm introduces loss constraint type pareto dominant rules and an adaptive evolution mechanism for solving.
  5. 5. The collaborative scheduling and optimizing control method of the multi-energy-flow optical storage and charging system according to claim 1, wherein in S4, rolling optimization scheduling is based on a model predictive control MPC framework, a predictive time domain multi-objective optimization model is built according to the current running state at each scheduling time t, a first time period scheduling strategy is executed after solving by adopting an improved NSGA-III algorithm, and the state of charge of an energy storage system is updated: Wherein the method comprises the steps of And And then rolling to enter the next scheduling moment, and circularly executing the optimization process.

Description

Multi-energy-flow optical storage and filling system cooperative scheduling and optimizing control method Technical Field The invention belongs to the technical field of comprehensive energy system optimal scheduling, and relates to a collaborative scheduling and optimal control method for a multi-energy-flow optical storage and charging system. Background Along with the rapid popularization of photovoltaic power generation and electric automobiles, the light storage and charge integrated power station system gradually becomes an important technical means for relieving the pressure of a power grid and improving the new energy consumption capability. The system integrates photovoltaic power generation, energy storage equipment and electric automobile charging facilities, can effectively optimize energy distribution and improves the utilization rate of renewable energy sources. However, optical storage and filling systems face multiple uncertainties and complex constraints during actual operation, which pose serious challenges for scheduling control. On one hand, the photovoltaic power generation output is obviously influenced by weather factors, has intermittence and randomness, and causes larger error in output prediction, and on the other hand, the charging load of the electric automobile presents obvious time fluctuation characteristic and aggregation effect, is easy to impact a power grid in a peak period, and increases the difficulty of system scheduling. In addition, the energy storage system plays an important role in energy regulation and buffering in the optical storage system, and the charge and discharge behaviors are jointly influenced by various constraints such as capacity, power, efficiency and service life loss, so that the complexity of the optimization problem is further increased. The existing optical storage and filling system dispatching method mostly adopts a single-target or weighted multi-target optimization strategy, and effective balance among a plurality of targets such as system income, power grid load smoothing, energy storage health and the like is difficult to realize. Meanwhile, the influence of prediction errors and the change of the system running state along with time is not fully considered in part of the method, and the online rolling optimization capability is lacked, so that the scheduling strategy has insufficient adaptability in actual running. In addition, when the traditional multi-objective evolutionary algorithm is used for solving the problems of different dimensionalities and multi-constraint scheduling, the problems of low convergence speed, unbalanced solution set distribution and the like are easy to occur, and the requirements of an optical storage and filling system on instantaneity and stability are difficult to meet. Therefore, how to construct a multi-objective optimal scheduling method of an optical storage and filling system with economical efficiency, stability and energy storage life under complex multi-constraint and uncertain conditions becomes a key technical problem to be solved at present. Disclosure of Invention Therefore, the invention aims to provide a collaborative scheduling and optimizing control method for a multi-energy-flow optical storage and filling system. In order to achieve the above purpose, the present invention provides the following technical solutions: A multi-energy-flow optical storage and filling system cooperative scheduling and optimizing control method comprises the following steps: S1, carrying out unified time scale processing on the time-sharing electricity price, the charging service price of the electric automobile, the charging load of the electric automobile and the photovoltaic power generation output data, and converting multi-source data into a continuous hour-level time sequence; S2, constructing six energy flow models of a photovoltaic-energy storage system, an energy storage-power grid, an energy storage-charging station, an energy storage-power grid and an energy storage-charging station by taking the photovoltaic system, the energy storage system, a power grid and a charging load as research objects; S3, constructing a multi-objective optimization model of the optical storage and charging system on the basis of an energy flow model, wherein the optimization objectives comprise maximization of system operation income, minimization of power grid load peak-valley difference and minimization of energy storage system loss rate; And S4, under the framework of model predictive control (Model Predictive Control, MPC), only executing a first time period scheduling strategy on an optimization result in each prediction time domain, updating the system state and the prediction data in real time, and circularly executing the steps to realize rolling optimization scheduling of the optical storage and filling system. The method comprises the steps of S1, collecting historical electric vehicle charging load data and photo