Optimal Stochastic Scheduling Strategy of Multi-vector Energy Complex Integrated with Full-blown Power-to-biomethane Model

We propose an optimal stochastic scheduling strategy for a multi-vector energy complex(MEC),considering a fullblown model of the power-to-biomethane(Pt M)process.Unlike conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane,a detailed Pt M model is introduced to emphasize the reactor kinetics and chemical equilibria of methanation.This model crystallizes the interactions between the Pt M process and MEC flexibility,allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic scenarios.Temperature optimization and flowsheet design of the Pt M process increase the average selectivity of methane(i.e.,ratio between net biomethane production and hydrogen consumption)up to 83.7%in the proposed synthesis flowsheet.Simulation results can provide information and predictions to operators about the optimal operating conditions of a Pt M unit while improving the MEC flexibility.

THE SINGLE MACHINE STOCHASTIC SCHEDULING WITH THE WEIGHTED JOB TARDINESS MINIMIZATION

This paper considers scheduling n jobs on a single machine where the job processing times anddue dates are independent random variables with arbitrary distribution functions.We consider the casethat the weighted job tardiness in expectation is minimized.It is assumed that job's due dates arecompatible with processing times and weights.We show that the jobs should be sequenced indecreasing stochastic order of their due dates.

Flexible Stochastic Scheduling of Microgrids with Islanding Operations Complemented by Optimal Offering Strategies

This paper presents a stochastic framework for optimal scheduling of microgrids(MGs)considering unscheduled islanding events,initiated by disturbances in the main grid.This scheduling approach considers different uncertainties and determines the day-ahead schedule of the resources considering emergency operations.The proposed strategy attempts to effectively manage demand and supply side resources to mitigate the effects of uncertainties in both normal and emergency operations.The prevailing uncertainties associated with renewable power generations,demand and electricity prices as well as uncertainties of islanding duration are addressed in the presented framework.The objective is to maximize the expected profit of the operator over the scheduling horizon,while restricting the risk of mandatory load shedding imposed by uncertain parameters within an acceptable level.According to the proposed strategy,an efficient probabilistic index is obtained from generation reserve margin(GRM)in islanded mode,and applied to create a proper offering price signal to coordinate responsive loads with renewable generations providing more reliable operations.The effectiveness of the proposed strategy in terms of economy and reliability is investigated via a comparison with other methods.Extensive numerical results illustrate that the proposed offering price strategy can improve the MG’s operation from both reliability and economic aspects.

Stochastic optimization of mine production scheduling with uncertain ore/metal/waste supply

Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming(SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the presence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29% higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.

Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

Stochastic Scheduling on an Unreliable Machine with General Uptimes and General Set－UpTimes

A model that,scheduling a set,of stochastic jods on a single machine that is subject to breakdorwn and repair is dicussed.The model assumes that a significant set-up time,which is an arbitrary variable,is when the machine changes from processing one of jobs to another job.When job i is being processed, the prooessing time of job i,uptime and reparied time of the machine are all general random variables. For both preemptive resume model and preemptive repeat model,we find the optimal polices that minimize the following objective functions:(1)the weighted sum of the completion times;(2)the weighted number of late jobs having random dud dates with exponential distributions.

Single machine stochastic JIT scheduling problem subject to machine breakdowns

In this paper we research the single machine stochastic JIT scheduling problem subject to the machine breakdowns for preemptive-resume and preemptive-repeat.The objective function of the problem is the sum of squared deviations of the job-expected completion times from the due date.For preemptive-resume,we show that the optimal sequence of the SSDE problem is V-shaped with respect to expected processing times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.We discuss the difference between the SSDE problem and the ESSD problem and show that the optimal solution of the SSDE problem is a good approximate optimal solution of the ESSD problem,and the optimal solution of the SSDE problem is an optimal solution of the ESSD problem under some conditions.For preemptive-repeat,the stochastic JIT scheduling problem has not been solved since the variances of the completion times cannot be computed.We replace the ESSD problem by the SSDE problem.We show that the optimal sequence of the SSDE problem is V-shaped with respect to the expected occupying times.And a dynamic programming algorithm with the pseudopolynomial time complexity is given.A new thought is advanced for the research of the preemptive-repeat stochastic JIT scheduling problem.

A Discrete Artificial Bee Colony Algorithm for Stochastic Vehicle Scheduling

Vehicle scheduling plays a profound role in public transportation.Especially,stochastic vehicle scheduling may lead to more robust schedules.To solve the stochastic vehicle scheduling problem(SVSP),a discrete artificial bee colony algorithm(DABC)is proposed.Due to the discreteness of SVSP,in DABC,a new encoding and decoding scheme with small dimensions is designed,whilst an initialization rule and three neighborhood search schemes(i.e.,discrete scheme,heuristic scheme,and learnable scheme)are devised individually.A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.

Day-ahead Scheduling of Multi-carrier Energy Systems with Multi-type Energy Storages and Wind Power

The integration of large-scale wind power brings challenges to the operation of integrated energy systems(IES).In this paper,a day-ahead scheduling model for IES with wind power and multi-type energy storage is proposed in a scenario-based stochastic programming framework.The structure of the IES consists of electricity,natural gas,and heating networks which are all included in the model.Operational constraints for IES incorporating multi-type energy storage devices are also considered.The constraints of the electricity network,natural gas network and heating network are formulated,and non-linear constraints are linearized.The calculation method for the correlation of wind speed between wind farms based on historical data is proposed.Uncertainties of correlated wind power were represented by creating multiple representative scenarios with different probabilities,and this was done using the Latin hyper-cube sampling(LHS)method.The stochastic scheduling model is formulated as a mixed integer linear programming(MILP)problem with the objective function of minimizing the total expected operation cost.Numerical results on a modified PJM 5-bus electricity system with a seven-node natural gas system and a six-node heating system validate the proposed model.The results demonstrate that multi-type energy storage devices can help reduce wind power curtailments and improve the operational flexibility of IES.

Optimal energy consuming planning for a home-based microgrid with mobility constraint of electric vehicles and tractors

This research deals with the energy management problem to minimize the cost of non-renewable energy for a small-scale microgrid with electric vehicles(EV)and electric tractors(ET).The EVs and ETs function as batteries in the power system,while they often have to leave it for their mobility and agricultural work.Each State of Charge(SoC),which is the charge rate of the battery from 0 to 1,and the operating time of ETs are optimized under the assumption that the required electrical energy,the arrival and departure time of EVs,and the working time of ETs are given by users,but they include uncertainties.In this paper,we deal with these uncertainties by constraints for robust energy planning and expected optimization based on scenarios,and show that the scheduling of the SoC assuming the worst case and the optimal home-based power consumption planning that considers the cost of each scenario corresponding to each variation can be obtained.Our proposed method is formulated as a mixed-integer linear programming(MILP),and numerical simulations show that the optimal cooperative operation among multiple houses can be obtained and its global optimal or sub-optimal solution can be quickly obtained by using CPLEX.