An innovative optimization approach for energy management of a microgrid system

The local association of electrical generator including renewable energies and storage technologies approximately installed to the client made way for a small-scale power grid called a microgrid. In certain cases, the random nature of renewable energy sources, combined with the variable pattern of demand, results in issues concerning the sustainability and reliability of the microgrid system. Furthermore, the cost of the energy coming from conventional sources is considering as matter to the private consumer due to its high fees. An improved methodology combining the simplex-based linear programming with the particle swarm optimisation approach is employed to implement an integrated power management system. The energy scheduling is done by assuming the consumption profile of a smart city. two scenarios of energy management have been suggested to illustrate the behaviour of cost and gas emissions for an optimised energy management. The results showed the reliability of the energy management system using an improvemed approach in scheduling of the energy flows for the microgrid producers, limiting the utility’s cost versus an experiment that had already been done for a similar system using the identical data. The outcome of the computation identified the ideal set points of the power generators in a smart city supplied by a microgrid, while guaranteeing the comfort of the customers i.e without intermetency in the supply, also, reducing the emissions of greenhouse gases and providing an optimal exploitation cost for all smart city users. Morover, the proposed energy management system gave an inverse relation between economic and environmental aspects, in fact, a multi-objective optimization approach is performed as a continuation of the work proposed in this paperinfo:eu-repo/semantics/publishedVersio

Multi-Objective Multi-mode Time-Cost Tradeoff modeling in Construction Projects Considering Productivity Improvement

In today's construction industry, poor performance often arises due to various factors related to time, finances, and quality. These factors frequently lead to project delays and resource losses, particularly in terms of financial resources. This research addresses the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP), a real-world challenge that takes into account the time value of money and project payment planning. In this context, project activities exhibit discrete cost profiles under different execution conditions and can be carried out in multiple ways. This paper aims to achieve two primary objectives: minimizing the net present value of project costs and project completion times while simultaneously improving the project's productivity index. To accomplish this, a mathematical programming model based on certain assumptions is proposed. Several test cases are designed, and they are rigorously evaluated using the methodology outlined in this paper to validate the modeling approach. Recognizing the NP-hard nature of this problem, a multi-objective genetic algorithm capable of solving large-scale instances is developed. Finally, the effectiveness of the proposed solution is assessed by comparing it to the performance of the NSGA-II algorithm using well-established efficiency metrics. Results demonstrate the superior performance of the algorithm introduced in this study.Comment: 40 pages, 20 figures, 7 table

Project Scheduling to Maximize Positive Impacts of Reconstruction Operations

Since the decline of the Cold War, the risk of major conflict between powerful industrialized nations has significantly decreased. Insecurity in the twenty-first century is forecast to arise rather from the debris of imploding states. Such situations may require intervention | military or otherwise | by concerned states, and the frequency with which these interventions occur is increasing. To meet this new operational challenge, the US military must adapt its planning procedures to account for Security, Stabilization, Transition, and Reconstruction Operations (SSTRO). This research develops a project scheduling based framework for post-conflict reconstruction that prioritizes and schedules reconstruction activities in such a way as to maximize the positive impacts during the initial phase of SSTRO. Specifically, this research proposes to build on the Multimode Resource Constrained Project Scheduling Problem with Generalized Precedence Relations (MM-RCPSP-GPR) using goal programming to maximize the reconstruction operations\u27 positive impact to the local population. This MM-RCPSP-GPR variant is applied to a notional example to illustrate its potential use in post-conflict SSTRO

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Variations in demographic correlates of contraceptive usage among married women in Nigeria (1999-2008)

This study examined the variations in demographic correlates and patterns of contraceptive usage among married women in Nigeria. The data used were the Nigeria Demographic and Health Surveys (NDHS) data set series for 1999, 2003 and 2008. Overall, 34,919 women in age group 15-49 years were covered in the ratio of 17:15:69 across the three data set respectively. The data sets were merged into a single file and analyzed using a combination of univariate and multivariate analytical techniques. The findings reflect, among others, a progressive increase in contraceptives usage from 1999 to 2008. The study confirmed among others that educational attainment, usual place of residence and age are universal determinants of contraceptive use while desired number of children could be intermediated with other factors to influence contraceptive use among women of child bearing age in Nigeria. The study recommends further campaign towards increasing usage of contraceptives in order to stem the growth rate of children ever born

Multi-Period Stochastic Resource Planning: Models, Algorithms and Applications

This research addresses the problem of sequential decision making in the presence of uncertainty in the professional service industry. Specifically, it considers the problem of dynamically assigning resources to tasks in a stochastic environment with both the uncertainty of resource availability due to attrition, and the uncertainty of job availability due to unknown project bid outcome. This problem is motivated by the resource planning application at the Hewlett Packard (HP) Enterprises. The challenge is to provide resource planning support over a time horizon under the influence of internal resource attrition and demand uncertainty. To ensure demand is satisfied, the external contingent resources can be engaged to make up for internal resource attrition. The objective is to maximize profitability by identifying the optimal mix of internal and contingent resources and their assignments to project tasks under explicit uncertainty. While the sequential decision problems under uncertainty can often be modeled as a Markov decision process (MDP), the classical dynamic programming (DP) method using the Bellman’s equation suffers the well-known curses-of-dimensionality and only works for small size instances. To tackle the challenge of curses-of-dimensionality this research focuses on developing computationally tractable closed-loop Approximate Dynamic Programming (ADP) algorithms to obtain near-optimal solutions in reasonable computational time. Various approximation schemes are developed to approximate the cost-to-go function. A comprehensive computational experiment is conducted to investigate the performance and behavior of the ADP algorithm. The performance of ADP is also compared with that of a rolling horizon approach as a benchmark solution. Computational results show that the optimization model and algorithm developed in this thesis are able to offer solutions with higher profitability and utilization of internal resource for companies in the professional service industry

International Space Station Traffic Modeling and Simulation

In an effort to provide NASA with an alternative perspective and some insights to the operational planning of the International Space Station (ISS), this research developed a simulation environment for the ISS and devised a method to evaluate various altitude strategies. The simulation environment allowed us to incorporate the natural random behaviors which affect the lifetime of objects in low earth orbit. We created prototype models of the operational planning process to analyze current altitude strategy approaches and acquire new strategies from insights observed. In addition, by extrapolating random future solar activity values from the interpolation of historical data, we established a spectrum of possible solar activity rather than just maximum, mean, and minimum values. From this process, we demonstrated a procedure to analyze a strategy using distributions of parameter outputs in response to random inputs