scholarly journals Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 144 ◽  
Author(s):  
Jianjian Shen ◽  
Xiufei Zhang ◽  
Jian Wang ◽  
Rui Cao ◽  
Sen Wang ◽  
...  
Keyword(s):  
Power Grid ◽  
Optimal Operation ◽  
Dry Season ◽  
Power Grids ◽  
Mixed Integer ◽  
Multi Objective Optimization ◽  
Hydropower Generation ◽  
Multi Objective ◽  
Hydropower System ◽  
Local Plants

This paper focuses on the monthly operations of an interprovincial hydropower system (IHS) connected by ultrahigh voltage direct current lines. The IHS consists of the Xiluodu Hydropower Project, which ranks second in China, and local plants in multiple recipient regions. It simultaneously provides electricity for Zhejiang and Guangdong provinces and thus meets their complex operation requirements. This paper develops a multi-objective optimization model of maximizing the minimum of total hydropower generation for each provincial power grid while considering network security constraints, electricity contracts, and plant constraints. The purpose is to enhance the minimum power in dry season by using the differences in hydrology and regulating storage of multiple rivers. The TOPSIS method is utilized to handle this multi-objective optimization, where the complex minimax objective function is transformed into a group of easily solved linear formulations. Nonlinearities of the hydropower system are approximatively described as polynomial formulations. The model was used to solve the problem using mixed integer nonlinear programming that is based on the branch-and-bound technique. The proposed method was applied to the monthly generation scheduling of the IHS. Compared to the conventional method, both the total electricity for Guangdong Power Grid and Zhejiang Power Grid during dry season increased by 6% and 4%, respectively. The minimum monthly power also showed a significant increase of 40% and 31%. It was demonstrated that the hydrological differences between Xiluodu Plant and local hydropower plants in receiving power grids can be fully used to improve monthly hydropower generation.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

scholarly journals Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4242 ◽  
Author(s):  
Van-Hai Bui ◽  
Akhtar Hussain ◽  
Woon-Gyu Lee ◽  
Hak-Man Kim
Keyword(s):  
Output Power ◽  
Wind Farm ◽  
Optimal Operation ◽  
Multi Objective Optimization ◽  
Power Fluctuation ◽  
Trade Off ◽  
Multi Objective ◽  
Optimal Output ◽  
Power Fluctuations ◽  
Optimal Set

In this paper, a multi-objective optimization method is proposed to determine trade-off between conflicting operation objectives of wind farm (WF) systems, i.e., maximizing the output power and minimizing the output power fluctuation of the WF system. A detailed analysis of the effects of different objective’s weight values and battery size on the operation of the WF system is also carried out. This helps the WF operator to decide on an optimal operation point for the whole system to increase its profit and improve output power quality. In order to find out the optimal solution, a two-stage optimization is also developed to determine the optimal output power of the entire system as well as the optimal set-points of wind turbine generators (WTGs). In stage 1, the WF operator performs multi-objective optimization to determine the optimal output power of the WF system based on the relevant information from WTGs’ and battery’s controllers. In stage 2, the WF operator performs optimization to determine the optimal set-points of WTGs for minimizing the power deviation and fulfilling the required output power from the previous stage. The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.

On Solving the Multi-Objective Software Package Upgradability Problem

2018 ◽  
Vol 9 (2) ◽  
pp. 18-38
Author(s):  
Noureddine Aribi ◽  
Yahia Lebbah
Keyword(s):  
Open Source Software ◽  
Software Package ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Software Systems ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Before And After ◽  
The Stability

Free and open source software (FOSS) distributions are increasingly based on the abstraction of packages to manage and accommodate new features before and after the deployment stage. However, due to inter-package dependencies, package upgrade entails challenging shortcomings of deployment and management of complex software systems, inhibiting their ability to cope with frequent upgrade failures. Moreover, the upgrade process may be achieved according to some criteria (maximize the stability, minimize outdated packages, etc.). This problem is actually a multi-objective optimization problem. Throughout the article, the authors propose a Leximax approach based on mixed integer linear programming (MILP) to tackle the upgradability problem, while ensuring efficiency and fairness requirements between the objective functions. Experiments performed on real-world instances, from the MANCOOSI project, show that the authors' approach efficiently finds solutions of consistently high quality.

scholarly journals Optimal Operating Schedule for Energy Storage System: Focusing on Efficient Energy Management for Microgrid

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 80 ◽  
Author(s):  
Sooyoung Jung ◽  
Yong Tae Yoon
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Power Grid ◽  
Optimal Operation ◽  
Operating Conditions ◽  
Mixed Integer ◽  
Small Scale ◽  
Basic Operation ◽  
Electricity Use ◽  
Operation Schedule

A microgrid is a group of many small-scale distributed energy resources, such as solar/wind energy sources, diesel generators, energy storage units, and electric loads. As a small-scale power grid, it can be operated independently or within an existing power grid(s). The microgrid energy management system is a system that controls these components to achieve optimized operation in terms of price by reducing costs and maximizing efficiency in energy consumption. A post-Industry-4.0 consumer requires an optimal design and control of energy storage based on a demand forecast, using big data to stably supply clean, new, and renewable energy when necessary while maintaining a consistent level of quality. Thus, this study focused on software technology through which an optimized operation schedule for energy storage in a microgrid is derived. This energy storage operation schedule minimizes the costs involved in electricity use. For this, an optimization technique is used that sets an objective function representing the information and costs pertaining to electricity use, while minimizing its value by using Mixed Integer Linear Programming or a Genetic Algorithm. The main feature of the software is that an optimal operation schedule derivation function has been implemented with MATLAB for the following circumstances: when the basic operation rules are applied, when operating with another grid, when the external operating conditions are applied, and when the internal operating conditions are applied.

scholarly journals A Decision-Making Tool for a Complex Post Enrollment-Based Course Timetabling

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Ngo Tung Son
Keyword(s):  
Combinatorial Problems ◽  
Mixed Integer ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Spring Semester ◽  
Multi Objective ◽  
Training Costs ◽  
Proposed Model ◽  
Decision Making Tool ◽  
Single Objective

The article describes a new method to construct an enrollment-based course timetable in universities, based on a multi-objective optimization model. The model used mixed-integer and binary variables towards creating a schedule. It satisfies students' preferences for study time, with the number of students in the same class being optimal for training costs while ensuring timetabling business constraints. We use a combination of compromise programming and linear scalarizing to transform many objective functions into single-objective optimization. A scheme of the Genetic Algorithm was developed to solve the proposed model. The proposed method allows approaching several types of multi-objective combinatorial problems. The algorithm was tested by scheduling a study schedule for 3,000 students in the spring semester of 2020 at FPT University, Hanoi, Vietnam. The obtained results show the average students' preference level of 69%. More than 30% of students have a satisfaction level of more than 80% of the timetable after two hours of execution time.

Multi-objective TMA management optimization using the point merge system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramazan Kursat Cecen
Keyword(s):  
Mathematical Model ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
First Come First Serve ◽  
Management Optimization ◽  
The Mathematical Model ◽  
Single Objective

Purpose The purpose of this study is to provide conflict-free operations in terminal manoeuvre areas (TMA) using the point merge system (PMS), airspeed reduction (ASR) and ground holding (GH) techniques. The objective is to minimize both total aircraft delay (TD) and the total number of the conflict resolution manoeuvres (CRM). Design/methodology/approach The mixed integer linear programming (MILP) is used for both single and multi-objective optimization approaches to solve aircraft sequencing and scheduling problem (ASSP). Compromise programming and ε-constraint methods were included in the methodology. The results of the single objective optimization approach results were compared with baseline results, which were obtained using the first come first serve approach, in terms of the total number of the CRM, TD, the number of aircraft using PMS manoeuvres, ASR manoeuvres, GH manoeuvres, departure time updates and on-time performance. Findings The proposed single-objective optimization approach reduced both the CRM and TD considerably. For the traffic flow rates of 15, 20 and 25 aircraft, the improvement of CRM was 53.08%, 41.12% and 32.6%, the enhancement of TD was 54.2%, 48.8% and 31.06% and the average number of Pareto-optimal solutions were 1.26, 2.22 and 3.87, respectively. The multi-objective optimization approach also exposed the relationship between the TD and the total number of CRM. Practical implications The proposed mathematical model can be implemented considering the objectives of air traffic controllers (ATCOs) and airlines operators. Also, the mathematical model is able to create conflict-free TMA operations and, therefore, it brings an opportunity for ATCOs to reduce frequency occupancy time. Originality/value The mathematical model presents the total number of CRM as an objective function in the ASSP using the MILP approach. The mathematical model integrates ATCOs’ and airline operators’ perspective together with new objective functions.

scholarly journals Multi-Objective Optimization for Order Assignment in Food Delivery Industry with Human Factor Considerations

2020 ◽  
Vol 12 (19) ◽  
pp. 7955
Author(s):  
Zhilan Lou ◽  
Wanchen Jie ◽  
Shuzhu Zhang
Keyword(s):  
Human Factor ◽  
Food Delivery ◽  
Mixed Integer ◽  
Operational Cost ◽  
Multi Objective Optimization ◽  
Workload Balancing ◽  
Multi Objective ◽  
Delivery Performance ◽  
Proposed Model ◽  
Order Assignment

The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.

scholarly journals Research on Short-term Multi-objective Optimization Scheduling oriented Peak Regulation of Power Network

2018 ◽  
Vol 246 ◽  
pp. 01026
Author(s):  
Mo Li ◽  
Yongqiang Wang ◽  
Xinwen Gao ◽  
Shen Qin
Keyword(s):  
Power Generation ◽  
Power Grid ◽  
Multi Objective Optimization ◽  
Short Term ◽  
Multi Objective ◽  
Generation Planning ◽  
Power Generation Planning ◽  
Cascade Hydropower Stations ◽  
Hydropower Stations ◽  
Optimization Scheduling

With the succession of river basins and inter-basin hydropower stations, the joint optimal operation of cascade hydropower stations in the river basin has large-scale, nonlinear, strong coupling, and multi-target characteristics, and must consider the effects of hydrometeorology, water demand, and power grid security. Focusing on the preparation of short-term power generation plans for cascade hydropower stations on the Qingjiang River, a comprehensive multi-objetive power generation planning model with the largest total power generation and the least load variance on the power grid is established. Based on the constraint processing method of multi-objective optimization scheduling in long-term, the optimal flow distribution technology is adopted to improve the accuracy of power generation planning. The above model is solved by using SMPSO. The results show that the improved algorithm can effectively overcome the shortcomings of slow convergence speed and easy convergence to local optimum. It can improve the power generation efficiency of the whole cascade while responding to the peaking demand of the power grid and provide a new solution to the short-term power generation planning ideas.

scholarly journals Simultaneous Minimization of Energy Losses and Greenhouse Gas Emissions in AC Distribution Networks Using BESS

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1002
Author(s):  
Federico Molina-Martin ◽  
Oscar Danilo Montoya ◽  
Luis Fernando Grisales-Noreña ◽  
Jesus C. Hernández ◽  
Carlos A. Ramírez-Vanegas
Keyword(s):  
Distribution Networks ◽  
Optimal Operation ◽  
Point Of View ◽  
Energy Losses ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Renewable Energy Resources ◽  
Efficient Operation ◽  
Multi Objective ◽  
Battery Energy

The problem of the optimal operation of battery energy storage systems (BESSs) in AC grids is addressed in this paper from the point of view of multi-objective optimization. A nonlinear programming (NLP) model is presented to minimize the total emissions of contaminant gasses to the atmosphere and costs of daily energy losses simultaneously, considering the AC grid complete model. The BESSs are modeled with their linear relation between the state-of-charge and the active power injection/absorption. The Pareto front for the multi-objective optimization NLP model is reached through the general algebraic modeling system, i.e., GAMS, implementing the pondered optimization approach using weighting factors for each objective function. Numerical results in the IEEE 33-bus and IEEE 69-node test feeders demonstrate the multi-objective nature of this optimization problem and the multiple possibilities that allow the grid operators to carry out an efficient operation of their distribution networks when BESS and renewable energy resources are introduced.

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