Generation Scheduling Research Articles

Environmentally sound short-term hydrothermal generation scheduling using intensified water cycle approach

This paper proposes an intensified water cycle approach (IWCA) to realize an environmentally sound optimal generation schedule for short-term hydrothermal systems over a day’s time horizon considering the conflicting economic and environmental aspects of thermal units. The equality constraints of the active power balance and full utilization of the available amount of water are handled independently using proportional sharing heuristics. The global solution accuracy and convergence rate of stochastic algorithms are significantly affected by parameter-tuning, exploration, and exploitation strategies. For intensifying the algorithm, opposition-based learning and local search are integrated with a chaotically tuned water cycle algorithm based on the natural flow of water toward the sea to balance exploration and exploitation. The numerical results show an improvement in the unified operating cost obtained from the price penalty factor to include the impact of gaseous pollutants and in the convergence performance metrics over the existing methods. The competence of the proposed approach is confirmed through illustrations of standard benchmark problems (unimodal, multimodal, and fixed dimension multimodal) and standard hydrothermal test systems. The performance of the proposed algorithm is substantiated through statistical significance tests, which include convergence curves, whisker box plots, and Wilcoxon signed-rank test.

Computationally efficient train timetable generation of metro networks with uncertain transfer walking time to reduce passenger waiting time: A generalized Benders decomposition-based method

With more and more interchange stations in a large-scale metro network, passengers tend to transfer between different metro lines from origination to destination, sometimes even more than once. Passenger waiting time is one of the critical standards for measuring the quality of urban public transport services. To support high service quality, this paper proposes a mixed integer nonlinear programming (MINLP) model for the train timetable generation problem of a metro network that minimizes the transfer waiting times and access passenger waiting times. In the mathematical formulation of the model, the transfer walking times at the interchange stations between two connected lines are treated as uncertain parameters. The robust train timetable generation model is formulated to optimize timetables by adjusting arrival and departure times of each train in the metro network to reduce access and transfer passenger waiting times. A robust counterpart is further derived that transforms the formulated robust model into a deterministic one. Moreover, a generalized Benders decomposition technique based approach is developed to decompose the robust counterpart into a subproblem and a master problem. The subproblem is a convex quadratic programming problem that can be solved efficiently. Finally, two sets of numerical examples, consisting of a small case and a large-scale case based on a real-world portion of the Beijing metro network, are performed to demonstrate the validity and practicability of the proposed model and solution approach.

An Annual Electric Energy Trade Scheduling Model under the Dual Track Mode

The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the “market electricity” and “planned electricity” double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of “market electricity” and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, “market electricity” capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China.

Benefit maximization and optimal scheduling of renewable energy sources integrated system considering the impact of energy storage device and Plug-in Electric vehicle load demand

The intermittent nature of renewable-based generation may cause the dip or rise in generation and load imbalances. This paperwork obtains optimal generation scheduling, market benefit maximization, and daily energy loss minimization considering the impact of Plug-in Electric vehicles (PEV) and battery energy storage devices using nonlinear programming. The Plug-in EV load demand has been modelled using the Monte Carlo simulation (MCS), and the size of the energy storage device is obtained in the renewable energy source (RES) Microgrid distribution system. The proposed work's main contributions are (i) total cost-benefit maximization, (ii) daily energy loss minimization, (iii) optimal generation scheduling of RES based Microgrid and (iv) probabilistic modelling of PEV load demand and BES sizing. Furthermore, the results were compared to those available in the literature. The voltage variations in the Microgrid are also calculated with the effects of PEV and BES into account. The IEEE-33 bus test system was the subject of the investigation. The multi-objective problem has been solved using iterative Monte Carlo Simulation (MCS) and Nonlinear Programming (NLP). The proposed approach was implemented using MATLAB and GAMS interface. The daily energy loss has been decreased by 30.085 %, and the voltage deviation (VD) has been minimized by1.165 %.

A baseline-reactive scheduling method for carrier-based aircraft maintenance tasks

Carrier-based aircraft maintenance tasks are conducted in time-critical, resource-constrained, and uncertain environments. Optimizing the scheduling allocation scheme of maintenance personnel and equipment, reasonably responding to uncertainty disturbances, and maintaining a high fleet availability are vital to the combat and training missions of carrier-based aircraft. The maintenance task scheduling problem for carrier-based aircraft is investigated in this study. First, a mathematical model for comprehensive carrier-based aircraft maintenance task scheduling that considers constraints such as maintenance personnel, equipment/shop, space, and parallel capacity is developed. Second, to generate the baseline scheduling scheme, an improved non-dominated sorting genetic algorithm II (I_NSGA-II) with local neighborhood search is proposed for the model optimization solution; I_NSGA-II uses the serial scheduling generation scheme mechanism to generate the time sequence scheduling scheme for maintenance personnel and equipment/workshop of different fleet sizes. Third, to cope with dynamic uncertainty disturbances, two reactive scheduling methods, i.e., complete rescheduling and partial rescheduling, are proposed to perform reactive scheduling corrections to the baseline schedule. Case simulation shows that the established mathematical model is reasonable and practical, and that the proposed I_NSGA-II is superior to the current mainstream algorithms. In addition, the decision maker can select between the two reactive scheduling methods flexibly based on the different forms and scales of disturbance.

Model predictive approaches for cost-efficient building climate control with seasonal energy storage

Due to the increasing share of fluctuating renewable energy, a seasonal energy storage with a high capacity is needed, which stores the summer surplus electricity to satisfy the winter heating demand.It is assumed that the control integration of a novel thermochemical seasonal energy storage concept into a building energy system can meet this requirement and save operating costs. For this purpose, a model-predictive control concept with a prediction horizon beyond that of public weather forecasts is crucial.A state-based model is developed consisting of a building, a water buffer and a heat supply. To assess the effects of seasonal storage, different heat supply configurations are considered. On this basis, two Model Predictive Control (MPC) concepts are designed to efficiently operate the system over one year. Since public weather forecasts are reliable in the time range of several days, test reference year data are used to approximate the weather forecast beyond the public forecast period. Additionally, the control hierarchy is comprised of a superordinate optimal generation scheduling (OGS) and a subordinate MPC. The concepts follow the scheduled long-term lime storage trajectory and realise possible short-term yields based on the current public forecast. The trajectory tracking is formulated either in the objective function or the constraints.The integration of the novel lime storage module into the heat supply of a building allows a reduction of operating costs of 18% in the realistic scenario and up to 80% in case of highly fluctuating electricity prices. This reduction potential is fully exploited by the developed control approaches, but it is very sensitive to the change of the controller parameters, the fluctuation of the electricity price and the weather data. Moreover, by applying the objective-based reference tracking approach, the higher-level scheduling hierarchy could be avoided.Through the results of this work, it is confirmed that the integration of a seasonal energy storage system can greatly reduce annual operating costs and that it is not crucial to apply a prediction horizon beyond that of public forecasts.

Optimal Virtual Power Plant Operational Regime Under Reserve Uncertainty

Virtual power plant (VPP) has become an important resource for reserve provision owing to its fast-responding capability. In this paper, an optimal VPP operational regime considering reserve uncertainty is proposed, which includes a novel day-ahead offering strategy and a real-time dispatching model. At the day-ahead stage, the offering strategy gives the VPP’s price-dependent offers in the energy market under multiple uncertainties on market price, renewable generation, and calls of reserve deployment. A hybrid stochastic minimax regret (MMR) model is proposed to facilitate making offering decisions in the electricity market. At the real-time dispatching stage, generation scheduling can be realized based on the MMR criterion in an online fashion. To alleviate the intrinsic conservativeness of the dispatching model, a self-adaptive algorithm is also proposed to instantly modify the confidence bounds. The proposed regime is comprehensively tested through extensive case studies, which demonstrate the effectiveness of our method in obtaining operational decisions that are less conservative.

Restoration Strategy for Active Distribution Systems Considering Endogenous Uncertainty in Cold Load Pickup

Cold load pickup (CLPU) phenomenon is identified as the persistent power inrush upon a sudden load pickup after an outage. Under the active distribution system (ADS) paradigm, where distributed energy resources (DERs) are extensively installed, the decreased outage duration can induce a strong interdependence between CLPU pattern and load pickup decisions. In this paper, we propose a novel modelling technique to tractably capture the decision-dependent uncertainty (DDU) inherent in the CLPU process. Subsequently, a two-stage stochastic decision-dependent service restoration (SDDSR) model is constructed, where first stage searches for the optimal switching sequences to decide step-wise network topology, and the second stage optimizes the detailed generation schedule of DERs as well as the energization of switchable loads. Moreover, to tackle the computational burdens introduced by mixed-integer recourse, the progressive hedging algorithm (PHA) is utilized to decompose the original model into scenario-wise subproblems that can be solved in parallel. The numerical test on modified IEEE 123-node test feeders has verified the efficiency of our proposed SDDSR model and provided fresh insights into the monetary and secure values of DDU quantification.

Analysis of Problems of the Power System with a High Proportion of Solar Generation

The rapid development of solar energy has led to the creation of qualitatively new energy systems with a high share of solar generation. The behavior of systems of this kind in certain cases differs significantly from the behavior of traditional energy systems containing the predominant share of thermal power plants. Thus, a significant share of the generation of solar power plants as part of the power system, the lack of an adjustment range for reactive power and the generally accepted concept of modeling a solar power plant lead to the problem of introducing unnecessary restrictions to maintain the stability and reliability of the power system. At the same time, the problems of stability and reliability must be solved simultaneously with the problems of saving energy resources; this is precisely the problem of optimization. The balance between reliability and economy lies in meeting the required restrictions on the cross-sections of the power system. The analysis made it possible to determine the priority directions in the study of the operation of solar power plants as part of a unified energy system. The calculation of the daily schedule of power generation for a set of SESs located in the same meteorological conditions for two seasons of the year has been performed. The real regulation range of reactive power was determined and the analysis of participation in the rating of stability and reliability of power systems was carried out. These results were obtained during the study of the power system of the Republic of Crimea and the city of Sevastopol. One of the main conclusions of this paper is the lack of a general approach in the description of SES together with the control system in mathematical models for calculating steady-state and transient electric power modes. Given the growing trend towards digitalization, this issue is currently becoming more acute. The conclusions made will allow us to set a vector for the development of the study of the issues of connecting the SES to the unified energy system.

Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids

Photovoltaic (PV) generation can drop sharply under reductions in solar irradiance due to cloud cover, resulting in adverse impacts on the performance of stand-alone microgrids. In addition, due to the stochastic nature of solar irradiance, it has remained challenging to forecast PV generation accurately. Therefore, a sufficient amount of operating reserve must be allocated for the variability and uncertainty of PV generation. However, excess operating reserve can decrease the economic efficiency of microgrids. This paper proposes an optimal generation scheduling and operating reserve management scheme for stand-alone PV-integrated microgrids, which comprises three core models. First, a two-stage recurrent neural network-based model is proposed for solar irradiance and cloud cover forecasting. Second, the operating reserve required for PV is managed more effectively based on weather conditions. Finally, an optimal generation scheduling model is applied based on the forecasting and operating reserve management models. The proposed scheme is applied to representative case studies to validate its advantages. The forecasting accuracy of the proposed model outperforms a conventional model. Under clear-sky conditions, the operating reserve for PV can be reduced down to 30% of PV output. Subsequently, the optimal operation scheduled by the proposed scheme can produce substantial savings of more than 5.5%.

Generation scheduling in power systems with high penetration of renewable energy

Share of renewable energy sources increased rapidly over last two decades primary as wind and solar power plants. Their increase was driven by governmental subsidies and priority access and dispatch regarding conventional units. Wind and solar power plants are inflexible sources because their generation depends on exterior, weather conditions and they cannot be controlled as conventional units. This paper will define term power system flexibility and provide an insight into flexibility of conventional and modern power systems. Detailed mathematical model of power system and all its components has been created and explained. Modeling has been executed as mixed integer linear program using Fico Xpress optimization suite. Using those models, flexibility analyses of power systems with different renewable energy sources share has been conducted

The Influence of Renewable Energy Sources and Electric Energy Storages on Grid Power Balances

In recent decades, increasingly more attention has been paid in our country and abroad to the use of renewable energy sources (RES). However, such factors as nonuniform distribution of RESs over the country territory and stochastic nature of their availability in time impede their wide-scale application. For RESs to be used on a wider scale, relevant regulatory and technical documents stipulating the electric power system designing should be developed, and a number of technical problems should be solved, e.g., those relating to the need of smoothing the electricity generation nonuniformity. In this study, distribution power grids containing RESs (wind and solar power plants) and electric energy storages are considered. The aim of the study is to develop proposals for a procedure of integrating RESs and energy storages into grids. The study results are supposed to be used in developing software and hardware solutions for control of an energy storage and predict the electricity consumption and electricity generation by RESs in power grids. In the study, mathematical modeling (computation experiment) methods were used for development of energy storage and RES models that take into account climatic factors (wind velocity, solar irradiance, and temperature) in certain geographical locations received from open data bases. For studying the grid power balance reliability, the operation of RESs within a generalized distribution grid is considered. The grid contains an equivalent centralized power system source, a distribution network, consumer loads, various types of RESs (wind and solar power plants), and a centralized electric energy storage. The power outputs produced by wind and solar power plants for a day (based on hourly averaged meteorological conditions) and month are analyzed for a number of regions in Russia; the annual amount of electricity generated by them is estimated, and the effect of smoothing the generation schedules due to combination of various types of RES and use of electric energy storages is evaluated. Universal models of RESs and electric energy storages for electric grid load flow analysis are suggested; the application of the developed models for power balance analysis of grids containing RESs and energy storages is illustrated; the key stages of introducing RESs and energy storage devices are identified and described, and tasks for which electrical operation modes have to be numerically analyzed are formulated.

Optimal unit-commitment generation scheduling using genetic algorithm: A case study of a 10-generator power system network

Generation Scheduling is a complex optimisation problem. The aim is to get an optimal combination of generating units for optimal operation. In this paper, Genetic Algorithm (GA) is presented as a viable optimisation tool to solve a fuel cost-based unit-commitment problem. The power system network adopted for the study is a 10-generator network. The prime objective here is to prepare the best economic start-up and shutdown schedules of the generators which meets the forecasted load demand plus reserve for a particular time interval while at the same time satisfying various system constraints. The implementation was done with the GA Toolbox in MATLAB 2018a. Results obtained were compared to the ones obtained with Lagrangian Relaxation optimisation technique and the comparison shows that Genetic Algorithm led to a slight reduction in fuel cost by ₦ 522,452.20 for the 24-hour period.

Hierarchically Coordinated Voltage Control in Seaport Microgrids Considering Optimal Voyage Navigation of All-Electric Ships

The uncoordinated scheduling and control of all-electric ships (AESs) will impose great challenges on voltage profiles in seaport microgrids. This article proposes a three-stage hierarchically coordinated voltage control strategy in seaport microgrids considering the optimal voyage navigation and scheduling of AES. In stage one, we first propose an arrival time estimation method for AES to reduce the potential voltage violation risk in different seaport microgrids. The optimal power flow (OPF) is then performed in each microgrid to determine the day-ahead dispatch of on-load tap changer (OLTC) and inverter-based renewable energy resources (RESs). In stage two, the optimal route planning is carried out based on the sea states and the Dijkstra algorithm. The voyage and generation scheduling of AES are formulated as optimization problems and solved by CPLEX solvers. In stage three, a real-time local voltage control strategy is performed to mitigate fast voltage violations. To fully exploit the voltage support capability of energy storage systems (ESSs) on berthed-in AES, both constant charging mode and voltage control mode are designed to regulate voltages while ensuring sufficient SOC levels of ESS when leaving the seaport. The simulation results verify the effectiveness of the proposed method both in saving the generation cost of AES and improving voltage profiles in seaport microgrids.

Evolutionary algorithm solution for economic dispatch problems

<span>A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints; these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed; the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.</span>

Web based application for Automatic Timetable Generation

Abstract: The manual means of getting ready time table in schools is exceptionally tedious and drawn- out task which typically winds up with different classes conflicting either at indistinguishable room or with same educators having more than each class in turn. Because of manualmethodology, legitimate utilization of assets is neither viable nor productive. To conquer this large number of issues we propose to make a mechanized framework with PC helped plan generator. The framework will take different sources of info like number of subjects, instructors, maximal talks an educator can direct, need of subject and points to be canvassedin a week or a talk, contingent on these information sources it will produce conceivable time tables for working days of the week, utilizing all assets such that will best suit the limitations.A suitable schedule is then looked over the ideal arrangements generated. Timetable creation is an extremely laborious and tedious assignment. To make plan it takes loads of persistence and worker hours. Time table is made for different purposes like to arrange addresses in school and universities, to make timing diagrams for train and transport plan and some more. To make schedule it requires loads of time and labor supply .In our paper we have attempted to diminish these hardships of producing plan by Heuristic Algorithm.

Kinerja Inert Gas Generator Untuk Mendapatkan Oksigen Konten 3% Pada Gas Lembam

Inert Gas System is a machine that functions as a security system to prevent explosions on tankers. Explosion prevention is carried out by introducing inert gas into the cargo tank with the aim of reducing oxygen levels in the cargo tank atmosphere so as to prevent the formation of a flammable gas mixture. Inert gas contains very little oxygen, namely 1-2% by volume. The device that produces the inert gas is called the Inert Gas Generator (IGG). The purpose of this study was to determine the factors that cause high oxygen content contained in the inert gas and the efforts made related to the factors mentioned so that the inert gas generator can work optimally. The research method used by the author is descriptive qualitative with Fishbone data analysis techniques and SWOT analysis. The results of the study stated that the factors causing oxygen content of more than 3% were dirty fuel filter, too much combustion air supply, oxygen analyzer that was rarely calibrated, flow sampling was too large, fuel quality was not good, lack of crew knowledge. to PMS (Plan Maintenance System) IGG. Efforts have been made to clean the fuel pump filter, close the air capacity valve a little, calibrate the oxygen analyzer according to the PMS schedule, reduce the sampling valve flow into the oxygen analyzer, add fuel oil treatment (additives) to the fuel, pay attention to the IGG maintenance schedule according to PMS, restore the maintenance implementation system based on the PMS inert gas generator schedule.

Determination of Emission Reduction Costs Through Optimization of Generator Scheduling in Indonesia

The energy crisis, the impact of emissions, the greenhouse effect, and climate change are global issues. The operation of power plants using fossil fuels impacts emissions that are harmful to the environment. This paper discusses the determination of the cost of reducing plant emissions, which is preceded by solving the problem of scheduling generators involving emissions (often known as Economic Emission Dispatch) for various conditions of variation in cost and emission weights using candidate area reduction techniques. This method was tested using the Java-Bali, Indonesia, 500 kV electrical system, 25 buses, with various loads. The ratio between the cost difference and the emission difference is determined in various conditions from the combination of costs and emissions. The simulation results are compared with other methods such as the firefly algorithm, Cuckoo algorithm, and particle swarm optimization. The results of these various methods show that it costs USD 167.3 to USD 218.1 to reduce emissions by 1 ton per 4 hours. This paper provides input to the government in making policies related to flexible power plant emission control. Considering that emission is a national issue in Indonesia, one of the efforts to reduce emissions from power plants is the implementation of policies related to the operation of plants by considering emissions. However, to avoid counter-productivity from the operation of electricity, the government can prepare funding to assist the electricity operator in implementing the policy for limiting the emission of the power plant.

A tree search heuristic for the resource constrained project scheduling problem with transfer times

In this work, we study the resource constrained project scheduling problem with transfer times, where the transfer of resources between activities takes a certain amount of time. For the problem, we propose an improved serial schedule generation scheme, a new lower bound, and a tree search heuristic. To generate a schedule, the improved serial schedule generation scheme iteratively schedules an activity by breaking some existing resource flows and decides the earliest start time in an efficient way. The new lower bound is adapted from the critical capacity lower bound by additionally considering resource transfer times. The tree search heuristic utilizes the improved serial schedule generation scheme to schedule an activity at its earliest start time and the new lower bound to prune unpromising nodes in the search tree. Several priority rules are introduced in the heuristic for the selection of the next activity during branching and also used in a greedy algorithm to compute an upper bound for node evaluation. Computational experiments on benchmark instances show that the improved serial schedule generation scheme reduces the computational time by 91.30% on average compared to an existing serial schedule generation scheme from literature, the new lower bound dominates the known adapted critical path lower bound and achieves an average improvement of 7.95%, and the tree search heuristic performs better than three existing algorithms for the problem.

A comparative study of modeling and solution approaches for the multi-mode resource-constrained discrete time–cost trade-off problem: Case study of an ERP implementation project

Most knowledge-intensive industries, especially companies developing software engineering projects such as Enterprise Resource Planning (ERP) implementation projects, generally necessitate finding the optimal trade-off between the project duration and total usage cost of the renewable resource costs (e.g., human resource expertise costs). Therefore, the MRC-DTCTP, which integrates classical multi-mode resource-constrained project scheduling (MRCPSP) and discrete time–cost trade-off problems (DTCTP), can be seen as a more applicable problem since it better reflects the objectives and requirements of today’s real-life software project applications. The MRC-DTCTP is a much more complex variant of the MRCPSP since it aims to minimize total direct/indirect costs of the resources simultaneously under a pre-specified project deadline. Based on this motivation, a new explicit integer-linear programming (ILP) model of the MRC-DTCTP was first developed based on the implicit non-linear programming model of Wuliang and Chengen (2009). Due to its NP-hard nature, we also proposed a constraint programming (CP) model that includes several search strategies to solve large-sized problem instances within reasonable computation time. In addition, a genetic algorithm (GA) approach in combination with a Modified Serial Schedule Generation scheme (SSGS) is implemented to make further comparisons on several benchmark instances, which are generated based on the existing MRCPSP data sets taken from the project scheduling problem library (PSPLIB) by considering additional problem characteristics. A comprehensive experimental study has shown that the proposed CP model and GA approach can provide superior results in shorter run times for large-sized benchmark instances. Finally, an international Enterprise Resource Planning (ERP) Software Company’s real-life application is presented. The ERP projects generally necessitate finding the optimal trade-off between project makespan and human resource costs, making the MRC-DTCTP much more difficult than classical MRCPSPs & DTCTPs. For further analysis, time–cost trade-off curves under different human resource availabilities and project deadlines are drawn to provide managerial insights to ERP project managers.