CN-122000919-A - Multi-time-scale electric-hydrogen thermal coupling wind-solar hydrogen storage system scheduling and configuration method

CN122000919ACN 122000919 ACN122000919 ACN 122000919ACN-122000919-A

Abstract

The invention relates to a scheduling and configuration method of a multi-time-scale electro-hydro-thermal coupling wind-solar hydrogen storage system, and belongs to the technical field of comprehensive energy system optimization. Aiming at the problems of time scale splitting, single energy-matter conversion, insufficient real-time response and the like of the traditional wind-solar hydrogen storage system, the invention constructs a three-layer closed loop collaborative framework of a cross-time scale optimizing layer, an energy-matter coupling hub module and a cross-layer-energy-matter coordination module, and realizes the integrated optimization of annual/quaternary capacity configuration, weekly/monthly mid-term planning and daily/real-time scheduling. Through explicit modeling of electric-hydrogen-thermal multipotency conversion and waste heat recycling, and combination of mixed integer programming and model predictive control technology, comprehensive energy utilization efficiency is remarkably improved while system operation stability is ensured. The invention adapts to the multi-time scale characteristics of wind and light output and load, can effectively reduce the running cost of the system, reduces the waste wind and waste light, and provides technical support for high-proportion renewable energy grid connection.

Inventors

Wang Gaijian
ZHOU TONG
ZHOU ZHUOYANG
ZHANG YANPING
ZHANG QUANLING
YANG JING
LI QING
ZHANG DENGFENG

Assignees

量云数字能源(内蒙古)有限公司
南京工业大学

Dates

Publication Date: 20260508
Application Date: 20260123

Claims (10)

1. The scheduling and configuration method of the multi-time-scale electro-hydrogen thermal coupling wind-solar hydrogen storage system is characterized by comprising the following steps of: S1, building an electro-hydrogen thermal coupling wind-light hydrogen storage system and acquiring system structural parameters and boundaries, wherein the system structural parameters and boundaries comprise a wind power/photovoltaic installation range, a battery/hydrogen storage/heat storage capacity range, rated power, efficiency and recovery coefficients of an electrolytic cell/fuel cell; s2, in the upper capacity configuration module, the capacity vector is used Constructing a year/season scale multi-objective optimization model for decision variables, and searching a pareto capacity solution set meeting constraints by adopting a non-dominant ranking genetic algorithm NSGA-II; s3, in the middle-layer medium-term scheduling module, planning parameters are used based on the capacity vector and the medium-and-long-term week/month scene data Establishing a week/month scale plan optimization model for the decision variables, and solving by adopting a particle swarm algorithm PSO to obtain optimized plan parameters Outputting the contract/budget, the reference inventory track and the reserve margin; s4, in a lower-layer short-term real-time scheduling module, based on the capacity vector, the optimized planning parameter, the short-term prediction data and the current system state, adopting a model prediction control MPC rolling mechanism at each rolling moment Constructing a prediction window The MILP scheduling model is linearly planned by mixed integers in the system and is solved by CPLEX to obtain a control sequence Executing the first control quantity And updating the system state; S5, constructing a cross-layer energy coordination module, and based on the operation key performance index KPI output by the lower layer, constraint violation statistics or solution of the optimal solution shadow price of the dual variable, dynamically correcting the planning parameters, updating the penalty factors in the upper layer capacity evaluation function, or returning to the upper layer to carry out reevaluation screening on the candidate capacity solution to form closed-loop collaborative optimization.
2. The multi-time scale electro-hydro thermal coupling wind-solar hydrogen storage system scheduling and configuration method according to claim 1, wherein in a cross-layer energy coordination module, the operation key performance indicators KPI comprise power rejection rate, heat load shortage rate, equipment start-stop times and energy purchasing cost deviation, and the constraint violation statistics comprise battery out-of-limit times, hydrogen storage stock out-of-limit times and heat storage stock out-of-limit times.
3. The multi-time scale electro-hydro-thermal coupling wind-solar hydrogen storage system scheduling and configuration method according to claim 2, wherein the specific steps of the upper capacity configuration module comprise: In terms of capacity vector Is a decision variable, wherein Is the wind power installed capacity, Is a photovoltaic installed capacity, Rated power of the electrolytic tank, Rated power of fuel cell, Is the energy storage capacity of the battery, Is hydrogen storage capacity, Is a thermal energy storage capacity; Randomly generating initial population individuals, wherein the capacity vector of each individual meets the upper and lower limit constraints; Taking the minimum annual total cost, minimum wind-discarding light-discarding rate and minimum outsourcing energy cost as multi-objective optimization targets, adopting a non-dominant ranking genetic algorithm NSGA-II to perform non-dominant ranking, crowding degree calculation, selection/crossing/mutation operation on the population, and after iteration is finished, obtaining an individual set with a non-dominant level as a first layer as a pareto capacity solution set; each candidate capacity solution needs to calculate the operation key performance index KPI by calling a middle-stage scheduling module and a lower-stage short-term real-time scheduling module; If the number of times of starting and stopping equipment of the candidate capacity solution exceeds a preset threshold, calling middle-layer and lower-layer estimators to recalculate KPIs, and reordering according to updated penalty factors, and if constraint violations exist in a plurality of candidate solutions, adjusting upper and lower limit constraints of capacity configuration, reevaluating the candidate solutions, and outputting a new pareto capacity solution set.
4. The multi-time scale electro-hydrogen thermal coupling wind-solar hydrogen storage system scheduling and configuration method according to claim 3, wherein the middle-stage medium-stage scheduling module comprises the following specific steps: Acquiring capacity vectors, medium-long period/month scene data and operation end data output by upper capacity configuration, wherein the medium-long period/month scene data comprises wind-light output prediction, electric/thermal load prediction and energy price prediction, and the operation end data comprises stock upper and lower boundaries and a standby margin range; The method comprises the steps of representing planning parameters by adopting low-dimensional parameterization, wherein the planning parameters comprise medium-term electricity purchase/hydrogen purchase contract quantity, battery SOC, reference track control points of hydrogen storage inventory and heat storage inventory and standby margin, the reference track control points of the battery SOC, the hydrogen storage inventory and the heat storage inventory are set to target states based on selected key time points, and a complete time sequence is generated for the obtained reference track control points through linear interpolation or smoothing functions; Taking the minimum middle-long running cost as an objective function, taking the low-dimensional parameter of the planning parameter as a particle position, and iteratively searching the optimal planning parameter through PSO; And carrying out projection restoration on the optimal planning parameters obtained by optimization, and ensuring that the optimal planning parameters are within a capacity configuration boundary and an operation constraint range.
5. The multi-time scale electro-hydro-thermal coupling wind-solar hydrogen storage system scheduling and configuration method according to claim 1, wherein the specific steps of the lower short-term real-time scheduling module comprise: At each scroll time Acquiring current system state and short-term prediction data, wherein the current system state comprises battery SOC, hydrogen storage inventory and heat storage inventory, and the short-term prediction data comprises wind-light output prediction, electric/heat load prediction and energy price prediction; Constructing a prediction window And control window ; Power of electrolytic cell Power of fuel cell Purchase electric power Electric power Battery charging power Battery discharge power Hydrogen storage and charging flow rate Hydrogen storage and discharge flow Thermal energy storage and heat storage power Heat storage and release power As continuous variable, mutually exclusive state of electricity purchase and sale Mutual exclusion state of battery charging and discharging Start-stop state of electrolytic tank Start-stop state of fuel cell As binary variables; Three-energy balance constraint, energy storage dynamic update constraint, equipment mutual exclusion constraint, equipment start-stop constraint, climbing constraint, stock upper and lower limits and power upper and lower limit constraint are adopted to construct MILP constraint, wherein the three-energy balance constraint comprises electric power balance constraint, hydrogen balance constraint and heat balance constraint, the energy storage dynamic update constraint comprises battery SOC update, hydrogen storage stock update and heat storage stock update, the equipment mutual exclusion constraint comprises electricity purchase mutual exclusion and battery charge and release mutual exclusion, and the equipment start-stop constraint comprises electrolyzer/fuel cell start-stop and power boundary binding; The MILP model is solved through CPLEX adopting a branch strategy of the strongest branch or pseudo-cost branch and a pruning condition of carrying out boundary pruning when the node relaxation lower bound is not better than the current optimal upper bound and carrying out feasible pruning when relaxation is not feasible, and a control sequence in a prediction window is output Executing the first control quantity Updating the state of the battery SOC and the state of a hydrogen storage/heat reservoir storage system, wherein the updated state is used as the initial state of the next rolling moment k+1; After the control quantity is scheduled and executed in a short term in real time, KPI, constraint violation statistics and shadow price are output to a cross-layer energy coordination module, the cross-layer energy coordination module judges to trigger a correction plan parameter, update a penalty factor or rescreen a candidate solution to adjust a middle layer and an upper layer according to feedback information, if the correction of the plan parameter is triggered, the middle layer updates the plan parameter and issues the plan parameter to a lower layer, if the update of the penalty factor is triggered, the upper layer adjusts a capacity evaluation function and reschedules the candidate capacity solution, if the rescreen is triggered, the upper layer eliminates an infeasible solution and outputs a new pareto solution set, and the lower layer reschedules the scheduling based on the new plan parameter or the candidate capacity solution and outputs new feedback information to form closed loop optimization.
6. The scheduling and configuration method of the multi-time scale electro-hydrogen thermal coupling wind-solar hydrogen storage system according to claim 5 is characterized in that the specific formulas of three-energy-balance constraint and energy storage dynamic update constraint in MILP constraint construction are as follows: Electric power balance constraint: ; Wherein, the The wind power at the moment t is represented, The photovoltaic power at the time t is represented, The wind and light discarding power at the time t is represented, The outsourcing electric power at the time t is indicated, The power selling at the time t is indicated, Representing the system electrical load demand at time t; hydrogen balance constraint: ; Wherein, the The hydrogen amount produced by the electrolytic cell at the time t is shown, Indicating the hydrogen purchased by the system from the outside at time t, Indicating the hydrogen gas sold to the outside by the system at time t, The hydrogen amount consumed by the fuel cell at the moment t is represented, and the hydrogen load demand of the system at the moment t is represented; thermal equilibrium constraint: ; Wherein, the Represents the waste heat recovery amount of the electrolytic tank at the time t, Represents the amount of waste heat recovery of the fuel cell at time t, Representing the system heat load demand at the time t; Battery SOC update: ; Wherein, the Representing the state of charge of the battery energy storage unit at time t, Indicating the conversion efficiency of the electric energy during the charging process, Representing the conversion efficiency of electric energy in the discharging process; Hydrogen storage inventory renewal: ; Wherein, the Indicating the inventory level of the hydrogen storage unit at time t; Hot bank storage updates: ; Wherein, the Indicating the inventory level of the thermal energy storage tank at time t, The conversion efficiency of heat in the heat storage and release processes is respectively, In time steps.
7. The scheduling and configuration method of the multi-time scale electro-hydrogen thermal coupling wind-solar hydrogen storage system according to claim 5 is characterized in that the specific formulas of equipment mutual exclusion constraint, equipment start-stop constraint and climbing constraint in MILP constraint construction are as follows: purchase electricity mutual exclusion: , wherein, the method comprises the steps of, For the outsourcing of the electric power at time t, The electric power is sold for the time t, To be a sufficiently large constant that the number of the elements, For the mutual exclusion state of electricity purchase and sale, the binary variable is 1=electricity purchase and 0=electricity sale; battery charging and discharging mutual exclusion: ; ; Wherein, the For the maximum charge power of the battery, For the maximum discharge power of the battery, A mutual exclusion state for battery charging and discharging, wherein the binary variable is 1=charging and 0=discharging; electrolytic tank start-stop state variable Binding constraint with power boundary: ; Wherein the method comprises the steps of 、 Respectively the minimum and maximum operating power of the electrolytic cell, For the power consumption of the electrolyzer at the moment t, The binary variable of the electrolytic tank is 1=start-up and running and 0=stop; Fuel cell start-stop state variable Binding constraint with power boundary: Wherein 、 Respectively the minimum and maximum operating power of the fuel cell, For the fuel cell power consumption at time t, The binary variable is 1=start-up and running and 0=stop; climbing constraint: ; ; Wherein, the For the maximum ramp down rate of the electrolyzer, For the maximum upward ramp rate of the electrolyzer, For the maximum ramp down rate of the fuel cell, Is the maximum ramp up rate of the fuel cell.
8. The scheduling and configuration method of the multi-time-scale electro-hydrogen thermal coupling wind-solar-hydrogen storage system is characterized by comprising at least a wind power generation unit, a photovoltaic power generation unit, a power grid interface unit, a battery energy storage unit, an electrolytic cell hydrogen production unit, a hydrogen storage unit, a fuel cell power generation unit, a waste heat recovery unit, a thermal energy storage unit, an electric load unit and a thermal load unit, wherein the electro-hydrogen-thermal three-energy deep coupling is realized by an energy coupling hinge module, and the energy coupling hinge module comprises an electrolytic cell hydrogen production model, an electrolytic cell waste heat recovery model, a fuel cell power generation model and a fuel cell waste heat recovery model.
9. The multi-time scale electro-hydro-thermal coupling wind-solar hydrogen storage system scheduling and configuration method of claim 8, wherein the core model formula in the energy coupling hub module is as follows: model for producing hydrogen by using electrolytic cell: wherein, the method comprises the steps of, The hydrogen amount produced by the electrolytic cell at the time t is shown, Indicating the efficiency of the electric energy conversion of the electrolyzer to hydrogen energy, The power of the electrolytic cell at the time t is expressed, Representing the time interval of the discretization, Represents the low-order heating value of hydrogen; and (3) an electrolytic cell waste heat recovery model: Wherein The waste heat recovery coefficient of the electrolytic tank; representing the waste heat proportion of the electrolytic cell; fuel cell power generation model: wherein, the method comprises the steps of, The fuel cell power at time t is indicated, Indicating the hydrogen energy conversion efficiency of the fuel cell, The hydrogen amount consumed by the fuel cell at time t is represented; Fuel cell waste heat recovery model: Wherein Is the waste heat recovery coefficient of the fuel cell, Indicating the waste heat ratio of the fuel cell.
10. The method for scheduling and configuring the multi-time scale electro-hydro-thermal coupling wind-solar hydrogen storage system according to any one of claims 1 to 9, wherein the specific priority rules of the dynamic correction plan parameters are as follows: when the heat load shortage rate is more than or equal to a preset threshold value, the charging priority of the heat energy storage is preferentially improved; Triggering the dynamic adjustment of a thermal energy storage reference inventory track when the thermal energy storage inventory constraint shadow price obtained by CPLEX solving is more than or equal to a preset threshold value, and preventing thermal load shortage in advance; when the electricity rejection rate is more than or equal to a preset threshold value, increasing the upper limit value of the energy storage/hydrogen storage reference inventory track; When the deviation of the purchase energy cost is more than or equal to a preset threshold value, the medium-term electricity purchase/hydrogen purchase contract quantity is adjusted.

Description

Multi-time-scale electric-hydrogen thermal coupling wind-solar hydrogen storage system scheduling and configuration method Technical Field The invention relates to the technical field of comprehensive energy system optimization scheduling and capacity planning, in particular to a scheduling and configuration method of a multi-time-scale electric-hydrogen thermal coupling wind-solar-hydrogen storage system. Background As the permeability of renewable energy sources such as wind and light is continuously improved, the randomness and fluctuation of the output of the renewable energy sources bring challenges to the stable operation of a power grid. The existing wind-solar battery/hydrogen storage system mostly adopts a capacity configuration-daily scheduling two-layer optimization framework, wherein the upper layer determines the installed and energy storage capacity of equipment through planning, and the lower layer develops daily economic scheduling within a given boundary. Such methods typically focus only on the electro-hydrogen unidirectional conversion link, with intra-day scale economic optimization as a core goal. However, the wind-solar energy resource and the load demand have obvious seasonal/weekly structural differences, the matching of long-term capacity allocation and actual operation is difficult to ensure from a single daily optimization view, and the resource mismatch problems such as 'big maraca' or equipment overload and the like are easy to occur. Meanwhile, a large amount of recoverable waste heat is generated in the processes of hydrogen production by the electrolytic cell and power generation by the fuel cell, and the existing model does not bring the waste heat into energy flow management, so that the comprehensive energy utilization efficiency of the system is low. In addition, the traditional static scheduling mechanism lacks real-time rolling optimization capability, and is faced with the problems of wind and light abandoning, energy shortage or outsourcing cost surge and the like caused by short-time abrupt scene response lag such as wind and light suddenly falling, load impact and the like. At present, a comprehensive energy system optimization method capable of achieving multi-time scale coordination, multi-energy deep coupling and real-time fluctuation response is needed in the industry so as to solve the bottleneck of the prior art in the aspects of long-term planning matching, energy utilization efficiency, operation stability and the like. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a scheduling and configuration method of a multi-time-scale electro-hydro-thermal coupling wind-solar hydrogen storage system, solves the problems of poor multi-time-scale adaptability, low energy utilization rate, insufficient real-time fluctuation response and cross-layer constraint coordination isolation of the traditional wind-solar hydrogen storage system, and realizes the integrated optimization of annual/quaternary capacity configuration, week/month plan and daily/real-time scheduling and the integrated closed-loop optimization and electro-hydro-thermal multi-energy deep coupling of capacity configuration, medium-term plan and real-time scheduling by constructing a three-layer closed-loop collaborative framework of a cross-time-scale optimization layer, an energy coupling hinge module and a cross-layer energy coordination module. Through explicit modeling of electric-hydrogen-thermal multipotency conversion and waste heat recycling, and combination of mixed integer programming and model predictive control technology, comprehensive energy utilization efficiency is remarkably improved while system operation stability is ensured. The technical scheme of the invention has strong reproducibility, is suitable for the multi-time scale characteristics of wind and light output and load, can effectively reduce the running cost of the system, reduces the wind and light abandoning and provides technical support for high-proportion renewable energy grid connection. In order to solve the technical problems, the invention provides a multi-time-scale scheduling and configuration method of an electro-hydrogen thermal coupling wind-solar hydrogen storage system, which comprises the following steps: S1, building an electro-hydrogen thermal coupling wind-light hydrogen storage system and acquiring system structural parameters and boundaries, wherein the system structural parameters and boundaries comprise a wind power/photovoltaic installation range, a battery/hydrogen storage/heat storage capacity range, rated power, efficiency and recovery coefficients of an electrolytic cell/fuel cell; s2, in the upper capacity configuration module, the capacity vector is used Constructing a year/season scale multi-objective optimization model for decision variables, and searching a pareto capacity solution set meeting constraints by adopting a non-dominant ranking genetic algorithm