Generation Scheduling Research Articles

An Autonomous Task Assignment Paradigm for Autonomous Robotic In-Space Assembly.

The development of autonomous robotic systems is a key component in the expansion of space exploration and the development of infrastructures for in-space applications. An important capability for these robotic systems is the ability to maintain and repair structures in the absence of human input by autonomously generating valid task sequences and task to robot allocations. To this end, a novel stochastic problem formulation paired with a mixed integer programming assembly schedule generator has been developed to articulate the elements, constraints, and state of an assembly project and solve for an optimal assembly schedule. The developed formulations were tested with a set of hardware experiments that included generating an optimal schedule for an assembly and rescheduling during an assembly to plan a repair. This formulation and validation work provides a path forward for future research in the development of an autonomous system capable of building and maintaining in-space infrastructures.

Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems

An essential challenge in generation scheduling (GS) problems of hydrothermal power systems is the inclusion of adequate modeling of the hydroelectric production function (HPF). The HPF is a nonlinear and nonconvex function that depends on the head and turbined outflow. Although the hydropower plants have multiple generating units (GUs), due to a series of complexities, the most attractive modeling practice is to represent one HPF per plant, i.e., a single function is built for representing the plant generation instead of the generation of each GU. Furthermore, due to the computation time constraints and representation of nonlinearities, the HPF must be given by a piecewise linear (PWL) model. This paper presented some continuous PWL models to include the HPF per plant in GS problems of hydrothermal systems. Depending on the type of application, the framework allows a choice between the concave PWL for HPF modeled with one or two variables and the nonconvex (more accurate) PWL for HPF dependent only on the turbined outflow. Basically, in both PWL models, offline, mixed-integer linear (or quadratic) programming techniques are used with an optimized pre-selection of the original HPF dataset obtained through the Ramer-Douglas-Peucker algorithm. As a highlight, the framework allows the control of the number of hyperplanes and, consequently, the number of variables and constraints of the PWL model. To this end, we offer two possibilities: (i) minimizing the error for a fixed number of hyperplanes, or (ii) minimizing the number of hyperplanes for a given error. We assessed the performance of the proposed framework using data from two large hydropower plants of the Brazilian system. The first has 3568 MW distributed in 50 Bulb-type GUs and operates as a run-of-river hydro plant. In turn, the second, which can vary the reservoir volume by up to 1000 hm3, possesses 1140 MW distributed in three Francis-type units. The results showed a variation from 0.040% to 1.583% in terms of mean absolute error and 0.306% to 6.356% regarding the maximum absolute error even with few approximations.

Real-time pipe system installation schedule generation and optimization using artificial intelligence and heuristic techniques

Infrastructure systems in the United States are aging and considerable investment is needed to renew and replace a significant proportion of the existing systems. Piping systems, which are used in many infrastructure systems such as the distribution networks for utilities – water, sewage, gas, oil, etc., are very important in this regard. Real time scheduling is an important and necessary task in the planning and execution of construction projects. This is of particular importance in the installation of pipe systems, for which it is time consuming to plan and coordinate between team members the detailed requirements and information for the generation of practical installation schedules. During the installation stage, there can be delays or interference that could lead to the failure of the initial schedule plan. Current approaches are time-consuming, not automated and do not provide real-time schedules. Thus, the process is still fragmented and essentially manual, with inefficient information flow. To effectively improve the installation schedule, current knowledge of the installation site situation is important, with this knowledge being used to generate realistic schedules. Artificial intelligence (AI) maximizes the value of data by learning from previous cases and facilitates decision-making by making the process smarter and automatic. This paper proposes a new AI framework with machine learning (ML) and heuristic optimization techniques for automating practical pipe system installation schedule generation and optimization. A BIM model is used as reference to provide pipe system component information. A hybrid knowledge-based system is developed to integrate data-driven knowledge base and site-driven knowledge base on pipe system installation. K-Nearest Neighbor (KNN) and Graph Neural Network (GNN) ML techniques are adapted to map extracted components with the installation activities and their requirements for installation based on knowledge obtained from industry experts and piping codes. In addition, a heuristic algorithm is adopted to optimize the installation schedule. Finally, an optimal installation schedule that minimizes overlapping activities, time and cost is suggested.

A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking- based Crow Search Algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search Algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual’s best solution (pbest) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

Solution of short term integrated hydrothermal-solar-wind scheduling using sine cosine algorithm

The combined scheduling of hydro, thermal and hybrid energy (i.e., wind and solar) is attracting more and more attention nowadays. This paper explores the contribution of renewable energy sources in the traditional hydrothermal scheduling problem in order to overcome the prevailing issues like high generation cost and more emissive particles of fuel. This paper presents a new wind-solar based generation scheduling model that reduces the aforementioned issues. The proposed hydrothermal-solar-wind scheduling (HTSWS) model is very effective. It can reduce the price rate of generation and different emissive particles of fuel of the traditional scheduling model. A solution approach i.e., robust sine cosine algorithm (SCA) is employed to determine the minimum generation cost and fuel emission based best generating schedule for traditional scheduling problem. The same approach has been carried out in the context of the suggested HTSWS problems. The different control parameters of the SCA have been properly utilized to balance the exploration and the exploitation phases. These parameters lead to determination of the near-optimal global solution. The results show that the combined application of renewable sources and hydrothermal units benefit both economic and emission goals of the utility houses with operating costs and pollutant emissions falling by 10% and 64%, respectively. Here, another two powerful algorithms (i.e., backtracking search algorithm and whale algorithm) have also been applied to determine the minimum values of generation cost, emission, and combined generation cost-emission. Effectiveness and robustness of the proposed algorithm have been presented by comparing the SCA based results with other established latest algorithms for the considered problems.

Optimal schedule generation for single-channel crude transfer using a multi-model approach

This paper presents three techniques for scheduling for crude transfer between a port and a refinery on a single pipeline in the presence of stringent flow constraints. The three techniques are based on metaheuristics (business rules), mixed integer linear programming and reinforcement learning. In addition to comparing the three techniques, we also demonstrate how knowledge gleaned from one technique (in our case, the metaheuristics) can be used to design an algorithm based on another technique (in our case, reinforcement learning). A novel feature of our approach to reinforcement learning, in particular, is the use of low-fidelity, reduced-order simulators for training the scheduler and supporting it with a post-processor based either on business rules or on integer programming for ensuring compatibility with the constraints. The set of constraints considered here includes temporal restrictions on the use of the pipeline, flow constraints in the tanks that feed the column distillation units in the refinery, and the need to ensure a certain minimum residence time for crude in a given tank for dewatering.

A Discrete Chaotic Jaya algorithm for optimal preventive maintenance scheduling of power systems generators

The main role of the optimal Generator Maintenance Scheduling (GMS) problem in power systems is to develop an optimal preventive maintenance scheduling of the generation part units. An optimal GMS provides power systems with higher operational reliability, extends the generators lifetime and reduces the cost of generators maintenance. The GMS problem is formulated as an optimisation problem. This problem should satisfy both load’s power demand and workforce constraints with ensuring the reliability of power systems at economical operation cost. The GMS problem has been studied for many years when exact mathematical methods have been used in the past to reach exact solutions for small-scale problems. However, these conventional mathematical approaches have many limitations and they suffer from unreasonable computational efforts as system dimension increases. Traditional approximate methods have been adopted to overcome the limitations of exact methods for medium-scale power systems. However, they provide approximate solutions and they require a large computational effort for wide-area systems of big dimensions. Recently, modern methods based on metaheuristics optimisation have taken a long part to solve the GMS problem and to overcome the limitations of approximate methods. In this paper, a proposed Discrete Chaotic Jaya Optimisation (DCJO) algorithm is employed to perform the preventive maintenance scheduling of electric power systems generators. The proposed algorithm is based on a cooperation between the discrete Jaya optimisation algorithm and a proposed move rule based on Chaotic Local Search (CLS) technique to improve both exploration and exploitation phases. The GMS problem is modelled based on the reliability criterion of an objective function of a sum of the squares of the reserves of generation. The optimisation process is performed through minimising an evaluation function of a weighted sum of the objective function and the penalty function for violations of the constraints. The proposed approach has been tested in a 21-unit test system over a planned horizon of 52 weeks in which the peak load is 4739 MW and the maximum generation is 5688 MW and there is a total number of 35 workforce available per week to perform the maintenance tasks. The proposed method has been compared through several statistical tests with recent algorithms of the related works. The obtained results show the effectiveness of the proposed algorithm for solving the GMS problem as compared to other recent algorithms. This approach can be relied at the present upon to solve maintenance scheduling problems of generation units in power systems.

Toward social equity access and mobile charging stations for electric vehicles: A case study in Los Angeles

Electric vehicles (EVs) are considered a substitute for fossil-fueled vehicles due to rising fossil fuel prices and accompanying environmental concerns, and their use is predicted to increase dramatically shortly. However, the widespread use of EVs and their large-scale integration into the energy system will present several operational and technological hurdles. In the energy industry, an innovative solution known as the EVs smart parking lot (SPL) is introduced to handle EV charging and discharging electricity and energy supply challenges. This paper investigates social equity access and mobile charging stations (MCSs) for EVs, where the owner of MCSs is the EV parking lot. Accordingly, a new self-scheduling model for SPLs is presented in this paper that incorporates scheduling of the MCSs as temporary charging infrastructures while considering social equity access and optimizes SPL energy generation and storage schedule. The main objectives of this research are to (i) develop MCSs accessibility measures and quantify the equity impacts of MCSs locations by modeling prioritized demand based on several indices; (ii) determine the optimal set-points of SPL components (i.e., combined heat and power (CHP), photovoltaic system, electrical and heat-energy storage, and MCSs) to manage electrical peak demand and to maximize the economic benefits of SPLs. Results indicate that the proposed demand prioritization function model can meet the required EV charging demands for prioritized events, and the self-scheduling model for SPLs satisfies the charging demand of the EVs in the SPL location. Also, the social equity access to the EV charging stations is satisfied by analyzing the operation of MCSs around the prioritized demand of the prioritized events and social equity access indices.

Building’s hourly electrical load prediction based on data clustering and ensemble learning strategy

The short-term power load forecasting of buildings plays a key role in peak demand management and power generation scheduling. Many studies have shown that ensemble learning strategies are more accurate and feasible in practice than individual predictive models. However, the common way processing massive data is still traditional, that is, training and testing them as a whole, ignoring data’s inter law and pattern difference, which probably hinders the further improvement of forecasting effect. In this study, the characteristics of building data are identified and classified before model construction. Fully considering the diversities of model structure and parameters, an adaptive ensemble learning strategy is proposed for short-term building electrical loads forecasting. The unsupervised K-means clustering and supervised KNN (K-nearest neighbors) classification methods are combined for data clustering. Total eleven different sub-learners are applied for ensemble learning and a variety of intelligent optimization algorithms are used for parameter adjusting. The overall prediction accuracy is derived through a fusion module. Two sets of short-term electrical load data of actual buildings are used for model verification. The results show that the data clustering based ensemble learning strategy has better forecasting accuracy compared to eleven individual models and previous reported predictions. The superior prediction accuracies in different two cases also verify the adaptability and generalization abilities of the proposed ensemble method.

A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

Optimizing the schedule of thermal generators is probably the most important task when the operation of power systems is managed. This issue is known as the unit commitment problem in operational research. It has been profoundly studied in the literature, where several techniques have been proposed to address a computationally tractable solution. In turn, the ongoing changes of paradigms in energy markets focus the attention on the unit commitment problem as a powerful tool to handle new trends, such as the high renewable energy sources penetration or widespread use of non-conventional energy-storage technologies. A review on the unit commitment problem is propo- sed in this paper. The easy understanding of the diverse techniques applied in the literature for new researchers is the main goal of this state-of-art as well as identifying the research gaps that could be susceptible to further developments. Moreover, an overview of the evolution of the Mixed Integer Linear Programming formulation regarding the improvements of commercial solvers is presented, according to its prevailing hegemony when the unit commitment problem is addressed. Finally, an accurate analysis of modeling detail, power system representation, and computational performance of the case studies is presented. This characterization entails a significant development against the conventional reviews, which only offer a broad vision of the modeling scope of their citations at most.

More Environmental Sustainability Routing and Energy Management for All Electric Ships

With the popularity of the electrification of marine transportation, strategic energy-saving and environment-friendly management is gaining more attention recently. This paper proposes a novel coordinated navigation routing and power generation scheduling model, which aims at making a compromise between investment cost, operation cost, and greenhouse gas emissions under the distributional robust ambiguity of photovoltaic. A maritime hybrid energy configuration that combines diesel generator (DG), battery energy storage system (BESS), fuel cell (FC), photovoltaic (PV), and the cold-ironing connection is presented with a real-world navigation routine from Dalian to Singapore, and the optimization problem is solved through a bi-level tri-objective differential evolution algorithm, where navigation parameters, ESS and FC capacity and weight between operation cost and emission functions, are optimized in the upper level and specific power generation scheduling is settled in the lower level. Six case studies are conducted to verify its effectiveness and accuracy, and the simulation results demonstrate the proposed method can further reduce the operation cost while minimizing air contamination.

Optimal Generator Scheduling Considering Limit of Generator Operation Through Generator Capability Curve

Optimal generator scheduling is needed to save costs in an operation power system. In other hands, the operation of the generator in optimal condition must be considering the safety of the generator itself. This study proposes an optimal generator scheduling using a new modification of particle swarm optimization (PSO) algorithm considering the safety of the generator operation by visualized the operation of a generator through the generator capability curve. In term of scheduling, the MIW-PSO is applied with characteristics of power generator, such as smooth cost function and non-smooth cost function considering valve-point effects. The effectiveness and feasibility of the MIW-PSO were presented through four case studies and compared with previous literature in terms of power output and total operating cost. A case study is applied to show the performance of the optimal scheduling with MIW-PSO considering the safety of the generator operation by visualized the operation of a generator through the generator capability curve. The obtained results demonstrating the excellent performance of the proposed optimal scheduling both off to get optimal total cost and monitoring the safety of generator operation as well.

Energy management system for a hybrid PV-Wind-Tidal-Battery-based islanded DC microgrid: Modeling and experimental validation

DC microgrids are becoming integral part of a modern power system due to the growing penetration of DC distributed energy resources and loads, with additional advantages in terms of power quality improvement. They can be used for remote area electrification including villages, mountains, and islands. In this paper, an islanded DC microgrid is considered as a case study for islands with tidal energy potential, which consists of renewable energy resources including solar, wind and tidal, along with Li-ion battery storage. To ensure the efficient operation, an optimal energy management system is proposed for resource scheduling and utilization. In this regard, this paper proposes a two-stage supervisory energy management system for optimal operation of PV/Wind/Tidal microgrid with one-time communication to avoid excessive use of communication bandwidth in real-time. The primary stage schedules the optimal energy share from each energy source thereby enhancing the efficiency of the operation. The secondary stage updates the decision strategies by analyzing aggregated scheduled generation and demand profiles to ensure an effective utilization. The proposed energy management model has been experimentally validated to prove its effectiveness in achieving optimized operation for an islanded DC microgrid. Word Count: 4380

Scheduling of battery energy storages in the joint energy and reserve markets based on the static frequency of power system

By using the battery energy storage (BES) as a fast, reliable, and controllable resource, the system operator can compensate for power mismatches via changing the generation and consumption in discharging and charging modes. However, BES could decrease the inertia of the grid and endanger the security of the system. Therefore, system operators require a scheduling model that takes into account both security and economic issues. This paper presents a linear model for the optimal scheduling of synchronous generators and BESs in the joint energy and reserve markets, based on the constraints of primary and secondary frequency services. In the proposed model, the technical limitations of synchronous generators and BESs, the frequency limitation of the grid, rate of change of frequency (RoCoF) of generators, and the RoCoF of the grid are considered as constraints of the optimization problem. Accordingly, the optimal scheduling of the resources is determined in a way that ensures the security criteria of the system are not violated after the contingency. The effectiveness of the proposed strategy is demonstrated by four case studies. Simulation results show that increasing the battery capacity by 4.68% of the total capacity of the system reduces the total frequency reserves, and total costs of the system by 13.21 and 2.96%, respectively. Consequently, system operators can reduce total operating costs and provide adequate security by deploying BESs.

Analysis of hybrid power plant scheduling system diesel/photovoltaic/microhydro in remote area

Currently, diesel generators that are still operating must be hybridized with renewable energy in order to save fuel expenditure. In addition, the generator scheduling system is also a solution to save fuel. This study aims to schedule the operation of a hybrid power plant diesel/photovoltaic/microhydro in the village of Rumah Sumbul in serving the load needs in order to obtain the best and economical system combination that is able to serve the needs of a continuous electrical load. Based on the results of the analysis using HOMER, the scheduling in the first scenario of diesel/microhydro is able to produce electrical energy of 35.6 MW/year. For the second scenario, the diesel/photovoltaic operation is capable of producing 12.20 MW of electrical energy/year. The two hybrid power plants have met the target for electricity production of 2 MW/year. Furthermore, the economical side of the diesel/microhydro combination is cheaper with operating costs of 467,545,800 IDR for 25 years with a profit of 601,150,000 IDR during the project, where the return on capital occurs in the 13 th year. operational costs of IDR 2,885,597,000 for 25 years with a profit of 607,009,000 IDR during the project in progress and a return on investment in the 13th year.

Optimal energy scheduling of storage-based residential energy hub considering smart participation of demand side

The scheduling of the energy generation is one of the main problems in the energy companies. The investigates to feed the demand are developing with new approaches such as demand side participation and onsite generation via storage systems. This paper proposed the optimal coordination framework of an energy scheduling in the storage-based residential energy hub (EH) system. This framework is performed as two-stage multi-criteria optimization approach using demand side participation. In the first stage, minimizing demand side's bill is formulated using electrical demand load curtailment (ELDC) strategy, whereby energy demand is decreased. The optimized energy demand in first stage optimization is implemented in second stage to solve multi-criteria problem such as minimizing the generation costs and maximizing the consumers’ satisfaction level. In addition to ELDC participation in first stage, onsite generation is provided via storage systems include electrical storage system (ESS), heat energy storage (HES) and hydrogen storage system (HSS) in second stage by demand side. Also, impact of the ELDC strategy on the storage systems lifetime in order to reducing energy consumption in the charge state of the storage systems is assessment. The proposed optimization approach is solved using shuffled frog leaping algorithm (SFLA). Since, in second stage optimization multi-criteria problem is considered. The desired optimal solution in Pareto frontier solutions is found by LINMAP and fuzzy procedures. Finally, numerical simulation is implemented in two case studies to demonstrate effectiveness of the proposed approach.

Optimal price-based and emergency demand response programs considering consumers preferences

This paper presents a pricing optimisation framework for energy, reserve, and load scheduling of a power system considering demand response (DR). The proposed scheduling framework is formulated as a reliability-constrained unit commitment program to minimise the power system operation costs by finding optimal electricity prices and optimal incentives while guaranteeing the reliability of the system during contingencies. Moreover, customers’ attitude toward the electricity price and incentive adjustment and the effect of their preferences on load scheduling and operation of the system are investigated in various DR programs. The proposed scheme is implemented on an IEEE test system, and the scheduling process with and without DR implementation is discussed in detail by a numerical study. The proposed method helps both the system operators and customers to reliably schedule generation and consumption units and select the proper DR program according to defined prices and incentives in the case of an emergency.

Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery

Optimizing day-ahead generation schedules of hydro–wind–photovoltaic (PV) complementary systems (HWPCSs) can help to promote the accommodation of wind and solar energies. However, it is challenging to formulate appropriate generation schedules for the large HWPCS that contains cascade hydropower plants, in particular, a steady requirement of power delivery is considered in the optimization model. To further improve complementary performance of the large HWPCS, we propose a risk-averse day-ahead generation scheduling approach that considers the steady requirement of power delivery. First, a representative scenario set is used to characterize forecast uncertainties of the wind and PV power. Then, a multi-objective optimal generation scheduling model with consideration of the operational risks of electricity curtailment and power shortage is proposed. Finally, a two-layer nested optimization framework is designed to derive the system’s generation schedule. The clean energy base in the upper Yellow River basin, China was selected as a case study. The results show that: (1) forecast uncertainties of wind and PV power are more likely to induce power shortage risk in summer and autumn, but to induce electricity curtailment risk in spring and winter; (2) without using extra constraint handling strategies, the proposed approach could directly yield a stair-shaped power delivery curve, which is good for long-distance power transmission applications; and (3) compared with a traditional method without considering the operational risks, the proposed generation scheduling approach could significantly reduce the comprehensive risk rate by 65% on average, while the cascade hydropower production and peak shaving performance are satisfactory. Therefore, the proposed approach is effective in guiding the day-ahead generation scheduling of the HWPCSs that contain cascade hydropower plants.

Long-Term Generation Scheduling: A Tutorial on the Practical Aspects of the Problem Solution

Long-Term Generation Scheduling: A Tutorial on the Practical Aspects of the Problem Solution

Optimizing Faculty Workloads and Room Utilization using Heuristically Enhanced WOA

The creation and generation of schedules that are free of conflicts manually every academic semester present higher education institutions with a duty that is laborious and demanding of their resources. The course timetabling optimization, as an education timetabling problem, is a popular example of an NP-hard combinatorial problem. Numerous attempts have been made over the course of the past few decades to find a solution to this problem, but no one has yet developed a foolproof approach that can examine all alternatives to find the best method. The promising swarm-based optimization algorithm called Whale Optimization Algorithm was heuristically enhanced in the present study and is called HEWOA. It was designed as a solution to the course timetabling problem discussed in the current study. HEWOA was able to generate an efficient timetable for the large dataset of 1700 events for an average time of 14.92 seconds only, with an average generation of 7.2 and a best time of 8.38 seconds. These results reveal that the performance of HEWOA was better than that of various hybrids of the Genetic Algorithm that was compared in the present study.