Short-term Scheduling Research Articles

Genetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertainty

ABSTRACTThe main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.

Short-term hydro scheduling of a variable speed pumped storage hydropower plant considering head loss in a shared penstock

In this paper we present in detail how to handle the nonlinear and state-dependent elements in the power consumption function (PCF) of a variable speed pump (VP). The function is formulated by piecewise linear approximations with dynamically specified breakpoints. In the proposed method, the head variation resulting from the change in the water level of the upstream and downstream reservoirs and head loss caused by the friction of water on the penstock wall are both considered. Furthermore, we put forward a heuristic to incorporate the head loss in a penstock shared by multiple VPs in a short-term hydro scheduling tool used for daily operation in the real world. The case study is taken from the Limmern pumped storage hydropower plant with 4×250 MW reversible Francis pump-turbines with variable speed technology. Each penstock is shared by two of the four units. The numerical results demonstrate that the appropriate formulation of the PCF and the accurate representation of the head loss in a shared penstock are crucial for obtaining realistic and optimal scheduling for VPs.

A comparison of linear interpolation and spline interpolation for turbine efficiency curves in short-term hydropower scheduling problems

The purpose of short-term hydropower scheduling (STHS) in a competitive electricity market is to maximize the profit in the near future while adapting to the mid-term strategies for system operation. To ensure a realistic and feasible operation for STHS, it is crucial to accurately represent the hydropower production function (HPF). The turbine efficiency can be represented by a two-dimensional table provided by the turbine manufacturers or measured by the hydropower producers on-site with some given efficiency values for a set of net water head and discharge values. In literature, the actual efficiency value for any working point is usually linearly interpolated from the given efficiency values. To the best of authors’ knowledge, no study has been done so far to investigate how a nonlinear interpolation for the turbine efficiency, i.e. spline interpolation, may contribute to the application of STHS. In this paper, we compare the performance of linear interpolation and spline interpolation for turbine efficiency curves in the STHS problem and the bidding strategy in intraday market. The interpolation methods are tested on an operational hydro-power scheduling model, Short-term Hydro Optimization Program (SHOP), which is used by most large hydropower producers in the Nordic countries. Numerical tests demonstrate superior behavior of spline interpolation for the estimation of turbine efficiency between measured points. Compared to linear interpolation, spline interpolation for turbine efficiency curves gives smoother discharge transitions in day-ahead scheduling and is vital for obtaining continuous marginal cost curves in intraday trading. Based on the results in this paper, spline interpolation is a promising alternative to linear interpolation for turbine efficiency curves in STHS.

Deep Learning-Based Multivariate Probabilistic Forecasting for Short-Term Scheduling in Power Markets

In the current competition framework governing the electricity sector, complex dependencies exist between electrical and market data, which complicates the decision-making procedure of energy actors. These must indeed operate within a complex, uncertain environment, and consequently need to rely on accurate multivariate, multi-step ahead probabilistic predictions. This paper aims to take advantage of recent breakthroughs in deep learning, while exploiting the structure of the problem to design prediction tools with tailored architectural alterations that improve their performance. The method can provide prediction intervals and densities, but is here extended with the objective to generate predictive scenarios. It is achieved by sampling the predicted multivariate distribution with a copula-based strategy so as to embody both temporal information and cross-variable dependencies. The effectiveness of the proposed methodology is emphasized and compared with several other architectures in terms of both statistical performance and impact on the quality of decisions optimized within a dedicated stochastic optimization tool of an electricity retailer participating in short-term electricity markets.

Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems

The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.

Multi-Objective Short-Term Integration of Hydrothermal Operation with Wind and Solar Power using Nonlinear Programming.

Rapid growth in the global economy and developments increases the energy demands and 80% of the primary energy demand satisfies by fossil fuels. Extensive utilization of fossil fuels and the paucity of precautionary measurements creates two major challenges one environmental pollution and second future energy crises. Contemplating the serious, issues we have to move our energy demands to renewable and sustainable sources, which have less impact on the environment and sustainable. This article presents optimization techniques by using MATLAB toolbox YALMIP and Interior Point Optimizer IPOPT nonlinear programming to achieve multiple objectives one minimize the operational and fuel cost of the thermal power plant. Second, maximize the benefits of solar and wind power by decreasing the discard solar and wind power. In our optimized model, we perform short-term 24-hour scheduling and operation of four-cascaded hydropower reservoirs optimally combine with three thermal power unit’s one wind power plant and one solar power plant, which balance the active power generation with power demand. The result we obtained shows that a significant difference in thermal cost ($), discard power and CPU time of both tolls FMINCON and IPOPT. Discussion on renewable energy sources their impact on the environment and their tendency (2013 to 2017). In the end, calculations are perform for CO2 emission and reduction of the proposed model and compared the results with different scenarios of case 1 to case 5. Results show eloquence contribution of renewable energy sources and their aftermath on the environment and sustainable.

Solution for short-term hydrothermal scheduling with a logarithmic size mixed-integer linear programming formulation

Short-term hydrothermal scheduling issue is usually hard to tackle on account of its highly non-convex and non-differentiable characteristics. A popular strategy for handling these difficulties is to reformulate the issue by various linearization techniques. However, in this process, a fairly large number of continuous/binary variables and constraints will be introduced, which may result in a heavy computational burden. In this study, a logarithmic size mixed-integer linear programming formulation is presented for this issue, that is, only a logarithmic size of binary variables and constraints will be required to piecewise linearize the nonlinear functions. Based on such a formulation, a global optimum is therefore can be solved efficiently. To remove the linearization errors and cope with the network loss, a derivable non-linear programming formulation is derived. By optimizing this formulation via the powerful interior point method, where the previous global solution of mixed-integer linear programming formulation is used as the starting point, a promising feasible solution is consequently attained. Numerical results show that the presented logarithmic size mixed-integer linear programming formulation is more efficient than the generalized one and when it is incorporated into the solution procedure, the proposed methodology is competitive with currently state-of-the-art approaches.

Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer

This paper presents a new short-term multi-objective complementary scheduling problem for hydro-thermal-renewable power systems (HTRPSs). The economic/emission objectives, different from traditional economic/emission load dispatch problems, consider the on/off status of thermal power units as well as the dispatched load among engaged units as the optimization variables under various complicated nonlinear constraints. To solve the model with hybrid optimization variables, a multi-objective hybrid grey wolf optimization algorithm is proposed, in which continuous and discrete optimization variables are encoded and optimized synchronously. A daily scheduling simulation example of a hybrid power system consisting of cascade hydropower stations, thermal power units, and renewable energy (RE) power plants is studied to test the proposed model and algorithm. The results not only demonstrate that the proposed algorithm can achieve the best Pareto front for economic/emission bi-objectives compared to its competitors, but also confirm that the obtained scheduling schemes are completely within the feasible domain. Moreover, the impact of RE capacity on the power system is analyzed. Results indicate that the joint operation of RE and hydropower stations benefit both the economic and emission objectives, and the operational costs and pollution emissions decrease by 11.9% and 17.4%, respectively, when the RE capacity increases from 50% to 100% in the hybrid system.

MILP Based Robust Short-Term Scheduling for Wind–Thermal–Hydro Power System With Pumped Hydro Energy Storage

The uncertainty of the wind power generation and complex constraints of the hydropower pose challenges for the short-term scheduling of coordinated wind power, thermal power, and cascaded hydroelectric system (WTHS). In this paper, a robust security-constrained unit commitment model is established for a WTHS. The proposed model ensures the utilization of wind power and economic return from the scheduling. Conservative adjustable uncertainty sets are used to characterize the uncertainty of wind power over temporal and spatial dimensions. In this model, pumped hydro energy storage (PHES) is incorporated to cope with the wind power fluctuations. A simplified affine policy is developed for the decision making of the adjustable variables. Based on a series of linearization techniques, the proposed model is formulated as a single-level mixed-integer linear programming (MILP) problem, where the numerical tests performed on the modified IEEE 30-bus, IEEE 118-bus, and Polish 2736-bus systems verify the effectiveness of the model. The comparative analyses quantitatively evaluate the contributions of the PHES in terms of economic performance and wind power accommodation. The test results reveal that the robustness of scheduling plans is enhanced by the use of the PHES, and the proposed approach is applicable to the large-scale power systems.

考虑回水效应的一库多级式水电站群短期优化调度模型

考虑回水效应的一库多级式水电站群短期优化调度模型

Integrated activity planning: a proven approach to optimising operations outcomes

Integrated activity planning (IAP) is a proven approach for dealing with complexity and uncertainty when operating complex production systems. IAP is driven by the need to deliver on production forecasts, optimise the decision making for the planning, scheduling and execution of activities impacting production, mitigate the risk associated with major shutdowns or bringing capital projects online and optimise interfaces to allow engineering, supply, maintenance and operations to work together seamlessly. Planning is typically spread across several time horizons, including long-term strategic business plans, medium-term program plans and short-term maintenance plans and operational activity scheduling. This paper provides a definition of IAP, a description of its value, a detailed case study from one of the world’s leading IAP implementations and a series of lessons learned.

Improved real coded genetic algorithm-based short-term hydrothermal generation planning

Real coded genetic algorithm (RCGA) and improved real coded genetic algorithm (IRCGA) have been applied for the solution of short-term hydrothermal scheduling problem. The improved technique has been developed and tested on a multi reservoir cascaded hydroelectric system having generation-load power balance, upper and lower limits on reservoir capacity, water discharge rate, water spillage rate, hydraulic continuity restriction and operating capacity limits of different hydro and thermal units. The water transport delay between connected reservoirs has also been taken into consideration. The performance of the proposed approach is validated with four test systems. The results of the proposed algorithm are compared with those of modified differential evolution (MDE), teaching learning-based optimisation (TLBO), clonal selection algorithm (CSA), improved fast evolutionary programming (IFEP), improved particle swarm optimisation (IPSO), and genetic algorithm (GA). From numerical results, it has been found that the IRCGA-based approach is able to provide better solutions in lesser computational time.

Improved real coded genetic algorithm-based short-term hydrothermal generation planning

Real coded genetic algorithm (RCGA) and improved real coded genetic algorithm (IRCGA) have been applied for the solution of short-term hydrothermal scheduling problem. The improved technique has been developed and tested on a multi reservoir cascaded hydroelectric system having generation-load power balance, upper and lower limits on reservoir capacity, water discharge rate, water spillage rate, hydraulic continuity restriction and operating capacity limits of different hydro and thermal units. The water transport delay between connected reservoirs has also been taken into consideration. The performance of the proposed approach is validated with four test systems. The results of the proposed algorithm are compared with those of modified differential evolution (MDE), teaching learning-based optimisation (TLBO), clonal selection algorithm (CSA), improved fast evolutionary programming (IFEP), improved particle swarm optimisation (IPSO), and genetic algorithm (GA). From numerical results, it has been found that the IRCGA-based approach is able to provide better solutions in lesser computational time.

A Petri Net-Based Heuristic Algorithm for Short-Term Vehicle Scheduling in a Vehicle Inspection System

Vehicle inspection systems (VIS) have been extensively used by automobile manufacturers, maintenance companies, and the traffic administrative departments for the inspection of safety, reliability, and other indicators of both newly produced and in-used vehicles. How to increase their productivity by effectively operating such systems is an interesting and crucial problem. Because of the space limitation of testing field in such a system, a vehicle must complete pre-set inspection items at multiple stations in sequence. Hence, it is very challenging to sequence the vehicles to be tested to shorten the unnecessary waiting time for vehicle testing. This paper investigates the vehicle scheduling problem of such systems, where the time for vehicle movement in the system can be ignored. The system is modeled by resource-oriented Petri net, a graphical and mathematical modeling tool. Based on the model, this work analyzes the testing cycle time and develops a heuristic algorithm to efficiently solve the vehicle scheduling problem in such a system. For the first time, a heuristic algorithm for generating an optimal vehicle queue for testing is proposed. Industrial examples are used to illustrate the proposed algorithm and results show that a significant reduction in total test turnaround time can be obtained in comparison with the existing methods.

Single Reference Grid Continuous-Time Formulation for Batch Scheduling

Abstract Short term scheduling of multipurpose batch processes has received growing attention over past decades. It concerns the optimal allocation of a set of limited resources to tasks over time in order to enhance the revenue of plants. This paper addresses the short term scheduling of batch processes through a continuous-time mixed integer linear programming (MILP) formulation based on the state-task network (STN) representation that allows to consider multiple intermediate due dates for market requirements. The proposed formulation can be classified as a slot-based approach that views the time horizon as a set of ordered blocks of unknown and variable lengths. Compared to previous similar approaches, it is simpler and leads to a smaller mathematical model without decoupling tasks from units. A few benchmark problems are used to illustrate the computational advantages of the proposed optimization approach.

Short-Term Scheduling Strategy for Wind-Based Energy Hub: A Hybrid Stochastic/IGDT Approach

This paper evaluates the scheduling problem for energy hub system consisting of wind turbine, combined heat and power units, auxiliary boilers, and energy storage devices via hybrid stochastic/information gap decision theory (IGDT) approach. Considering that energy hub plays an undeniable role as the coupling among various energy infrastructures, still it is essential to be investigated in both modeling and scheduling aspects. On the other hand, penetration of wind power generation is significantly increased in energy infrastructures in recent years. In response, this paper aims to focus on the hybrid stochastic/IGDT optimization method for the optimal scheduling of wind integrated energy hub considering the uncertainties of wind power generation, energy prices and energy demands explicitly in a way that not only global optimal solution can be reached, but also volume of computations can be lighten. In addition, by the proposed hybrid model, the energy hub operator can pursue two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. This method optimizes energy hub scheduling problem in uncertain environment by mixed-integer nonlinear programming. This formulation is proposed to minimize the expected operation cost of energy hub where different energy demands of energy hub would be efficiently met. The forecast errors of uncertainties related to wind power generation and energy demands are modeled as a scenario, while an IGDT optimization approach is proposed to model electricity price uncertainty.

Robust stochastic optimal short-term generation scheduling of hydrothermal systems in deregulated environment

Hydrothermal systems play a significant role in electric energy systems as important power generation units, which have been studied in previous researches considering remarkable efforts. The optimal short-term hydrothermal scheduling (STHS) aims to attain optimal production scheduling of thermal and hydro plants for determining minimum operation cost of providing demand in the determined time interval. Different constraints should be studied in the solution of such issue containing limitations associated with water discharge, water storage, power production of plants, power balance of the system and water balance of hydro plants. In addition, valve impacts of thermal plants and complex hydraulic coupling of hydro plants are the other operational and technical constraints. In this study, the robust stochastic STHS is studied considering market price and demand uncertainties. Accordingly, robust optimization method is employed in this study to model price uncertainties. In addition, the uncertainties of load demand are handled using scenario-based modeling procedure. The scheduling problem of hydrothermal system is studied in a deregulated environment, where the hydrothermal system belongs to the private company and is capable to sell the surplus generated power to the market. Accordingly, the company aims to obtain maximum profit by selling power to market in addition to supplying power demand of the company. The introduced scheme for stochastic robust STHS is simulated and the provided solutions are investigated to verify the effectiveness of the scheme.

A branch and price algorithm for EOS constellation imaging and downloading integrated scheduling problem

Earth observation satellite (EOS) constellation imaging and downloading integrated scheduling (EOSCIDIS) is a challenging optimization problem with several engineering applications. Given a large number of imaging requests and a constellation consisting of a group of EOSs, a workable schedule must be developed for each EOS to allow for imaging and downloading in the most efficient manner given a certain scheduling horizon. No studies have proposed exact algorithms for the EOSCIDIS problem, although it has considerable potential for applications. In this paper, we developed a branch and price (B&P) algorithm for the EOSCIDIS problem. First, we decomposed the problem by extracting the sub-structure of the problem, and the decomposition resulted in a master problem and multiple pricing problems. Solving the linear relaxed master problem with column generation (CG) method provides a very tight upper bounds for the primal problem. We then embedded the CG process into a branch and bound (B&B) framework to find optimal integer solutions. In order to accelerate the convergence of the algorithm, we developed a heuristic utilizing the generated columns to construct feasible integer solutions to prune B&P tree branches earlier. We tested the B&P algorithm on highly-simulated instances that are constructed according to the Chinese Gaofen-1 satellite and 3 types of random instances which correspond to short, medium and long term scheduling respectively. Both results show that our method could find high-quality solutions with optimality gap less than 10% for most instances using reasonable computational costs .

Energy-efficient scheduling scheme with spatial and temporal aggregation for small and massive transmissions in LTE-M networks

Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing/uploading behaviors will result in periodic and/or event-driven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, due to the frequent communication nature of sensing data for IoT applications, the power consumption is increasing dramatically. To reduce the power consumption of IoT devices, the 3rd Generation Partnership Project (3GPP) has defined the discontinuous reception/discontinuous transmission (DRX/DTX) mechanism to allow devices to turn off their radio interfaces and go to sleep in various patterns. However, how to optimize the DRX/DTX scheduling while improving the resource efficiency is still an open issue. In this paper, we investigate an uplink resource allocation problem over long-term evolution machine-to-machine (LTE-M) networks, which is standardized by 3GPP to improve performance on IoT. In this network, we consider the periodic, event-driven, and query-based IoT traffic while minimizing the devices’ power consumption. We prove this problem to be NP-complete and propose an aggregation-efficient DRX/DTX scheduling (AEDS) scheme. This scheme takes advantage of both spatial and temporal data aggregation while applying DRX/DTX for energy saving. Specifically, the scheme consists of three phases. The first phase exploits long-term static scheduling for periodic data to ensure the latency and data rate while minimizing the devices’ wake-up time. The second phase tries to decrease devices’ power consumption through precisely determining their DRX/DTX configurations. Finally, the third phase employs short-term dynamic scheduling for event-driven and query-based data to improve transmission efficiency. Therefore, both small data and power consumption problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency, enlarge network capacity while reducing power consumption compared to the existing schemes.

Couple-based particle swarm optimization for short-term hydrothermal scheduling

A novel couple-based particle swarm optimization (CPSO) is presented in this paper, and applied to solve the short-term hydrothermal scheduling (STHS) problem. In CPSO, three improvements are proposed compared to the canonical particle swarm optimization, aimed at overcoming the premature convergence problem. Dynamic particle couples, a unique sub-group structure in maintaining population diversity, is adopted as the population topology, in which every two particles compose a particle couple randomly in each iteration. Based on this topology, an intersectional learning strategy using the partner learning information of last iteration is employed in every particle couple, which can automatically reveal useful history information and reduce the overly rapid evolution speed. Meanwhile, the coefficients of each particle in a particle couple are set as distinct so that the particle movement patterns can be described and controlled more precisely. In order to demonstrate the effectiveness of our proposed CPSO, the algorithm is firstly tested with four multimodal benchmark functions, and then applied to solve an engineering multimodal problem known as STHS, in which two typical test systems with four different cases are tested, and the results are compared with those of other evolutionary methods published in the literature.