Short-term Scheduling Research Articles

Robust hydrothermal unit commitment: A mixed-integer linear framework

The paper presents the info-gap theory for the sake of developing a robust framework for short-term hydrothermal scheduling to tackle severe load uncertainty. Deploying this method, the system operator is provided with a robust decision-making strategy to guarantee the minimum cost under load variation condition while practical and technical limitations such as dynamic ramp rate are taken into consideration. For this purpose, the proposed Unit Commitment (UC) problem considering all above advantages would be modeled in a linear framework, which is in turn taken into account as another outstanding feature of this study as it is compatible to apply to real-world systems. In order to investigate the model efficiency, the modified version of the IEEE 118-bus test system having 54 thermal beside 8 hydro plants is chosen as the case study. Eventually, the results demonstrate how demand fluctuations and errors in the predicted load can be tolerated by allocating additional robust cost.

An Online Learning Algorithm for Demand Response in Smart Grid

Demand response program with real-time pricing can encourage electricity users toward scheduling their energy usage to off-peak hours. A user needs to schedule the energy usage of his appliances in an online manner since he may not know the energy prices and the demand of his appliances ahead of time. In this paper, we study the users’ long-term load scheduling problem and model the changes of the price information and load demand as a Markov decision process, which enables us to capture the interactions among users as a partially observable stochastic game. To make the problem tractable, we approximate the users’ optimal scheduling policy by the Markov perfect equilibrium (MPE) of a fully observable stochastic game with incomplete information. We develop an online load scheduling learning (LSL) algorithm based on the actor-critic method to determine the users’ MPE policy. When compared with the benchmark of not performing demand response, simulation results show that the LSL algorithm can reduce the expected cost of users and the peak-to-average ratio in the aggregate load by 28% and 13%, respectively. When compared with the short-term scheduling policies, the users with the long-term policies can reduce their expected cost by 17%.

A Non-Dominated Sorting TLBO Algorithm for Multi-Objective Short-Term Hydrothermal Self Scheduling of GENCOs in a Competitive Electricity Market

A Non-Dominated Sorting TLBO Algorithm for Multi-Objective Short-Term Hydrothermal Self Scheduling of GENCOs in a Competitive Electricity Market

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

Fixed head short-term hydrothermal scheduling in presence of solar and wind power

A probabilistic short-term hydro-thermal-wind-photovoltaic scheduling based on point estimate method (PEM) is proposed in this article. To model the uncertainties associated with wind and solar power, point estimate method is used. The Weibull and Beta distributions are employed to handle the uncertain input variables. The mean generation cost of the system is optimized based on an optimization algorithm named crow search algorithm (CSA). Three test systems have been taken, the first test system contains only hydro and thermal plants, and rest of the two systems are based on wind and solar including hydro and thermal unit to investigate the effect of renewable energy sources in the selected test systems. Furthermore, underestimation and overestimation of available wind power has also been included in the problem. The simulation results show that when the penetration of renewable energy sources increases, the mean generation cost decreases. The results obtained by CSA have been compared with other well-known methods. Moreover, the accurate distribution of generation cost for the next day-ahead can be found out using Gram-Charlier series expansion.

Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine

Short-term electricity price forecasting in deregulated electricity markets has been studied extensively in recent years but without significant reduction in price forecasting errors. Also demand-side management and short-term scheduling operations in smart grids do not require strictly very accurate forecast and can be executed with certain practical price thresholds. This paper, therefore, presents a multikernel extreme learning machine (MKELM) for both short-term electricity price forecasting and classification according to some prespecified price thresholds. The kernel ELM does not require the hidden layer mapping function to be known and produces robust prediction and classification in comparison with the conventional ELM using random weights between the input and hidden layers. Further in the MKELM formulation, the linear combination of the weighted kernels is optimized using vaporization precipitation-based water cycle algorithm (WCA) to produce significantly accurate electricity price prediction and classification. The combination of MKELM and WCA is named as WCA-MKELM in this work. To validate the effectiveness of the proposed approach, three electricity markets, namely PJM, Ontario and New South Wales, are considered for electricity price forecasting and classification producing fairly accurate results.

Nondominated sorting-based disruption in oppositional gravitational search algorithm for stochastic multiobjective short-term hydrothermal scheduling

This article presents a novel framework of nondominated sorting-based disruption in oppositional gravitational search algorithm (NSDOGSA) for solving an inventive stochastic multiobjective short-term hydrothermal scheduling (SMSHTS) problems. An innovative SMSHTS mathematical formulation is derived in terms of stochastic objective functions subject to stochastic hydro- and thermal constraints. Also, a versatile constraint handling procedure is proposed to satisfy all the constraints. Besides, an opposition-based learning perception is assimilated in a gravitational search algorithm (GSA) to explore the excellence of the present population and disruption operator is integrated to hasten the convergence of solutions. Moreover, a nondominated sorting procedure is hybridized to attain a set of nondominated solutions for SMSHTS problems. In addition, an elite external archive is created to keep the nondominated solutions with the help of spread indicator and also to guide the search process toward global optima. Further, a fuzzy decision making is employed for selecting the best trade-off solution among the nondominated solution set. Finally, the proposed NSDOGSA approach is validated on four test systems that consist of two fixed-head and two variable-head stochastic multiobjective hydrothermal scheduling problems. Thus, the obtained simulation results are found to be better in terms of objective function values as well to satisfy constraints compared to other methods within a reasonable execution time.

Impact of CCGT Start-Up Flexibility and Cycling Costs Toward Renewables Integration

The large-scale introduction of variable and limitedly predictable renewables requires flexible power system operation, enabled by, i.a., dynamic power plant operation, storage, demand response, and enhanced interconnections. The fast start-up capabilities of combined-cycle gas turbines (CCGTs) are crucial in this regard. However, these nonstandard operating conditions significantly reduce the lifetime of critical turbine components, as reflected in long-term service agreements (LTSAs). This should also be reflected in short-term scheduling models. In light of this challenge, we apply a unit commitment model that allows multiple start-up loading modes while accounting for the corresponding turbine maintenance costs based on LTSAs. Leveraging this model, we investigated the need for fast start-up capabilities of a set of CCGTs as part of a small-scale test system considering various shares of renewables and dynamic reserve requirements. We have found that fast starts are often cost-optimal despite their greater turbine maintenance costs and a cost reduction of around 1% is obtained when considering more costly fast start-up modes when scheduling. Furthermore, cost-optimal reserve sizing is a function of the planning frequency and is reduced by fast starting capabilities. We conclude that taking advantage of fast start-up capabilities benefits the electricity generation system and yields a significant cost reduction.

Approximate Dynamic Programming for Large-scale Unit Commitment Problems

Unit commitment (UC) is crucial for reducing the generation cost of short-term power system scheduling. This study proposes a new approximate dynamic programming (ADP) method to solve large-scale UC considering ramp rate limits of thermal units. To solve the “curse of dimensionality,” a policy iteration algorithm using post-decision state variables is utilized for value function approximation. The post-decision state variables dramatically simplify the algorithm by avoiding the need to compute the value of all feasible states within Bellman’s equation. The proposed method is applied to systems ranging in size from 10 to 1000 units to optimize the generation scheduling of the units in a day. And the time horizon of the UC problem is split into 96 time periods. It is shown that ADP can converge to promising results in polynomial time complexity for large-scale UC.

RETRACTED: Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: An epsilon constraint method and fuzzy satisfying approach

Abstract In this paper, the short-term scheduling of fuel cell and combined heat and power based microgrid by considering cost and emission minimization and different demand response program, as a multi-objective microgrid dispatch problem, is studied. In order to reduce operational cost, time of-use and real time pricing demand response programs have been modeled, and the effects of these programs on the load profile have been discussed. The Pareto solutions in multi objective model are generated by using the e-constraint method which transforms the multi-objective optimization problem into a single-objective problem and the optimal solution has been chosen by implementing fuzzy satisfying approach. The microgrid contains a fuel cell unit, two combined heat and power generation units, power-only unit, boiler, battery storage system, and heat buffer tank. The application of the proposed method is investigated and three cases have been studied. The first case is solved without considering demand response programs. In other two cases time-of-use rate demand response and real time pricing demand response are considered respectively and the impact of demand response programs on microgrid scheduling is discussed. To model the proposed cost-emission operation scheduling of microgrid a mixed-integer linear program is used and solved by GAMS software.

A note on short-term scheduling of multi-grade polymer plant using DNA computing

Short-term scheduling of batch polymer plant involves the scheduling of different orders in parallel available production lines. The scheduling becomes more challenging due to the presence of sequence-dependent changeover constraints between different orders which lead to combinatorial optimization formulation. Such combinatorial optimization problems have exponential time complexity on the silicon-based computer. DNA computing experiments are found to be promising for such combinatorial optimization problems particularly involving unique feasible optimal solution. However, use of DNA to find a solution to real-life problems involving multiple feasible solutions is an emerging area of research. The present paper illustrates the DNA solutions to the short-term scheduling of a polymer plant involving multiple feasible solutions and parallel production lines. The DNA computer aided with nearest neighbour heuristics and iterative implementation found to be successfully searching the optimal solution in a combinatorial search space for three short-term scheduling problems of multi-grade polymer plant.

An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems

This paper proposes an effectively adaptive selective cuckoo search algorithm (ASCSA) for solving short-term hydrothermal scheduling problems with available water constraint, reservoir volume constraints, and transmission network constraints. The proposed ASCSA is a newly improved version of the conventional cuckoo search algorithm to enhance the solution quality and reduce the maximum number of iterations based on two new techniques including the new ratio of the difference between the fitness function values and the integration of solutions into one group. The effectiveness of ASCSA has been validated via eight hydrothermal systems, in which the last two systems consisting of the IEEE 30-bus and IEEE 118-bus systems are considered with a set of constraints in the transmission network. To investigate the performance of ASCSA, several algorithms are also implemented in the paper such as conventional cuckoo search algorithm, modified cuckoo search algorithm, particle swarm optimization, global vision of particle swarm optimization with inertia weight, differential evolution, and improved differential evolution. From result comparisons of the test systems, the proposed ASCSA method has obtained lower total costs than other methods implemented for solving the problems. Therefore, the proposed ASCSA is a very efficient and favorable method for solving the considered hydrothermal scheduling problems.

A stochastic mid-term scheduling for integrated wind-thermal systems using self-adaptive optimization approach: A comparative study

In the optimal and economic operation of the power system, generation scheduling is an essential task. Conventional short-term generation scheduling does not regard the huge important operational issues related to the generators, such as initial enterprise costs, maintenance costs, fuel availability, monthly load, etc. Hence, due to the time horizon scheduling of the daily short-term generation scheduling, it is not optimal in the long-term operation while considering the mentioned effects. In this context, this paper proposes a stochastic higher level of scheduling named Stochastic Mid-Term Generation Scheduling of Wind-Thermal systems by considering fixed and variable maintenance costs of generators units. In the proposed model, the 2m + 1 Point Estimate Method is applied to accurately evaluate the uncertainty related to the operation cost wind power and the load uncertainties for the proposed problem. To effectively solve it, a heuristic algorithm named Adaptive Modified Cuckoo Search Algorithm is employed with a novel self-adaptive Wavelet mutation tactic. To assess the performance of the proposed algorithm on solving the problem, the results are compared with the latest algorithms presented in the literature. Numerical results confirm the efficiency and superiority of the 2m + 1 point estimate method model and stability of the novel adaptive modified cuckoo search algorithm on solving the stochastic mid-term generation scheduling of wind-thermal systems problem.

Optimal Control Algorithms and Their Analysis for Short-Term Scheduling in Manufacturing Systems

Current literature presents optimal control computational algorithms with regard to state, control, and conjunctive variable spaces. This paper first analyses the advantages and limitations of different optimal control computational methods and algorithms which can be used for short-term scheduling. Second, it develops an optimal control computational algorithm that allows for the solution of short-term scheduling in an optimal manner. Moreover, qualitative and quantitative analysis of the manufacturing system scheduling problem is presented. Results highlight computer experiments with a scheduling software prototype as well as potential future research avenues.

Batch-centric scheduling formulation for treelike pipeline systems with forbidden product sequences

This paper presents a new mixed-integer linear programming (MILP) formulation for the short-term scheduling of liquid transportation by pipeline. It relies on a continuous-time representation featuring a single grid and is suitable for treelike systems with one input node simultaneously feeding multiple output nodes. The novelty is the incorporation of constraints for rigorously enforcing forbidden product sequences and ensuring a single batch inside idle segments. We tackle a real-life problem from the Iranian pipeline network, generating an optimal schedule that increases pipeline capacity by 6.2%, meeting demand from the weekly plan, fourteen hours earlier. We also solve three benchmark problems from the literature. The results show a significantly better performance than a closely related, state-of-the-art, product-centric formulation, which is unable to find a feasible solution for a problem that can now be solved to proven global optimality. It is primarily because multiple batches can enter/leave segments during a time slot.

A novel two-swarm based PSO search strategy for optimal short-term hydro-thermal generation scheduling

The optimal power generation of hydro-thermal systems to minimize total operating costs is one of the most important targets in short-term hydro-thermal scheduling (STHS) and its evaluation is complicated by several complex and interconnected equality and inequality constraints.To solve this type of problem, it is necessary to develop on one side a model of the system characterized by a fair and proper treatment of all the equality and inequality constraints and on the other a performing optimization algorithm simultaneously considering not only the minimization of the operating costs but also the feasibility of the proposed solution.The paper presents a novel two-swarm based PSO (Particle Swarm Optimization) search strategy developed for solving these kinds of STHS problems. To exclude constraint violations as far as possible during the optimization process, first a complete inequality and equality constraints treatment of the hydrothermal systems was developed, combined with a fair spillage modelling. Then, a recent evolution of the particle swarm optimization algorithm ASD-PSO (Adaptive Search Diversification-Particle Swarm Optimization) was modified, introducing a secondary swarm able to exploit the potential contribution of the infeasible solutions. Within the context of this new strategy, two different swarms were designed, each characterized by a different treatment of the infeasible solutions, and each interacting properly with the other at every iteration.The primary swarm dealt with feasible solutions, resulting from the manipulation of constraint repairing strategies, and with a limited number of infeasible solutions whose potential contribution in the search process was not wasted out but it was properly weighted on the basis of the violation degree. The secondary swarm involved less manipulated solutions that were subjected only to one repair strategy in order to foster the exploration capability of the swarm. At each iteration, the two swarms independently update their position and velocity but partially interact with each other to obtain mutual advantages from their different peculiarities.This novel two-swarm based strategy for solving short-term hydro-thermal scheduling (STHS) problems was validated by comparing the achieved performance in terms of accuracy with those of several algorithms proposed in literature. Six different test cases, widely analysed in the literature and characterized by increasing complexity, were considered. In all the considered Test Cases, the algorithm performed very well with minimum total fuel costs that were still lower than all the feasible solutions proposed in the literature, and that satisfied all the equality and inequality constraints of the system.

Improved harmony search algorithm for the solution of non-linear non-convex short-term hydrothermal scheduling

The short-term hydrothermal scheduling (SHTS) problem is defined as one of the most important and challenging scheduling problems, which aims to obtain the minimum cost of electrical energy production. Considering an insignificant generation cost of hydropower systems, the objective of STHS is to minimize the operation cost of thermal generation plants. The optimal generation scheduling of hydro and thermal units is a complex non-convex optimization problem taking into account a set of equality and inequality constraints. The challenging constraints of the STHS problem are valve-point loading effect of thermal units, power transmission loss of the system, generation capacity of thermal and hydro units, and load balance. In this paper, an improved harmony search (IHS) optimization algorithm is employed on the non-linear non-convex STHS problem. The proposed method is employed on two test systems in order to evaluate the performance of the applied optimization method on the STHS problem. The simulation results are provided and compared with those reported in recent publications, which proves the effectiveness of the proposed IHS method in the solution of the STHS problem.

Modified Differential Evolution Algorithm: A Novel Approach to Optimize the Operation of Hydrothermal Power Systems while Considering the Different Constraints and Valve Point Loading Effects

This paper proposes an efficient and new modified differential evolution algorithm (ENMDE) for solving two short-term hydrothermal scheduling (STHTS) problems. The first is to take the available water constraint into account, and the second is to consider the reservoir volume constraints. The proposed method in this paper is a new, improved version of the conventional differential evolution (CDE) method to enhance solution quality and shorten the maximum number of iterations based on two new modifications. The first focuses on a self-tuned mutation operation to open the local search zone based on the evaluation of the quality of the solution, while the second focuses on a leading group selection technique to keep a set of dominant solutions. The contribution of each modification to the superiority of the proposed method over CDE is also investigated by implementing CDE with the self-tuned mutation (STMDE), CDE with the leading group selection technique (LGSDE), and CDE with the two modifications. In addition, particle swarm optimization (PSO), the bat algorithm (BA), and the flower pollination algorithm (FPA) methods are also implemented through four study cases for the first problem, and two study cases for the second problem. Through extensive numerical study cases, the effectiveness of the proposed approach is confirmed.

Scheduling of Multiple Autonomous Guided Vehicles for an Assembly Line Using Minimum Cost Network Flow

This paper proposed a parallel automated assembly line system to produce multiple products having multiple autonomous guided vehicles (AGVs). Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs. Scheduling of AGVs to service the assembly lines and the corresponding stations are purposed. In the proposed problem the assignment of multiple AGVs to different assembly lines and the stations are performed using minimum-cost network flow (MCF). It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as MCF problem during each short-term scheduling window.

Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices

Daily increasing use of tidal power generation proves its outstanding features as a renewable source. Due to environmental concerns, tidal current energy which has no greenhouse emission attracted researchers’ attention in the last decade. Additionally, the significant potential of tidal technologies to economically benefit the utility in long-term periods is substantial. Tidal energy can be highly forecasted based on short-time given data and hence it will be a reliable renewable resource which can be fitted into power systems. In this paper, investigations of effects of a practical stream tidal turbine in Lake Saroma in the eastern area of Hokkaido, Japan, allocated in a real microgrid (MG), is considered in order to solve an environmental/economic bi-objective optimization problem. For this purpose, an intelligent evolutionary multi-objective modified bird mating optimizer (MMOBMO) algorithm is proposed. Additionally, a detailed economic model of storage devices is considered in the problem. Results show the efficiency of the suggested algorithm in satisfying economic/environmental objectives. The effectiveness of the proposed approach is validated by making comparison with original BMO and PSO on a practical MG.