Bidding strategy for a virtual power plant for trading energy in the wholesale electricity market

Virtual power plants (VPPs) are an effective way to increase renewable integration. In this PhD research, the concept design and the detailed costs and benefits of implementing a realistic VPP in Western Australia (WA), comprising 67 dwellings, are developed. The VPP is designed to integrate and coordinate an 810kW rooftop solar PV farm, 350kW/700kWh vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and smart appliances through demand management mechanisms. This research develops a robust bidding strategy for the VPP to participate in both load following ancillary service (LFAS) and energy market in the wholesale electricity market in WA considering the uncertainties associated with PV generation and electricity market prices. Using this strategy, the payback period can be improved by 3 years (to a payback period of 6 years) and the internal rate of return (IRR) by 7.5% (to an IRR of 18%) by participating in both markets. The daily average error of the proposed robust method is 2.7% over one year when compared with a robust mathematical method. The computational effort is 0.66 sec for 365 runs for the proposed method compared to 947.10 sec for the robust mathematical method. To engage customers in the demand management schemes by the VPP owner, the gamified approach is adopted to make the exercise enjoyable while not compromising their comfort levels. Seven gamified applications are examined using a developed methodology based on Kim’s model and Fogg’s model, and the most suitable one is determined. The simulation results show that gamification can improve the payback period by 1 to 2 months for the VPP owner. Furthermore, an efficient and fog-based monitoring and control platform is proposed for the VPP to be flexible, scalable, secure, and cost-effective to realise the full capabilities and profitability of the VPP

Maximising the value of supply chain finance

Supply Chain Finance (SCF) arrangements aim to add value by taking a cooperative approach to financing the supply chain. Interest in SCF has been increasing, and decision makers need a comprehensive view of possible applications and their potential. By means of theoretical and empirical exploration, we develop a conceptual framework that allows for positioning of SCF concepts and practices. The framework is based on a delineation of four archetypal SCF policies and the criteria that are relevant for adoption of each policy. The two main contributions of our framework are: (1) it explicitly considers operational motives as well as the financial motives that could prompt a firm to engage financial cooperation; and (2) it uses a discounted cash flow approach to illustrate the trade-offs that arise from different risks in SCF implementations. We use the framework to review policies that have been used in reverse factoring, an SCF practice that has recently become popular. Our study reveals implications for all the parties involved in an SCF implementation

International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book

The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions. This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more

Academic Year 2019-2020 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

An excerpt from the Dean\u27s Message: There is no place like the Air Force Institute of Technology (AFIT). There is no academic group like AFIT’s Graduate School of Engineering and Management. Although we run an educational institution similar to many other institutions of higher learning, we are different and unique because of our defense-focused graduate-research-based academic programs. Our programs are designed to be relevant and responsive to national defense needs. Our programs are aligned with the prevailing priorities of the US Air Force and the US Department of Defense. Our faculty team has the requisite critical mass of service-tested faculty members. The unique composition of pure civilian faculty, military faculty, and service-retired civilian faculty makes AFIT truly unique, unlike any other academic institution anywhere

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Air Force Institute of Technology Research Report 2018

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Large-Scale Optimization for Interdependent Infrastructure Systems

The primary focus of this thesis is to develop decomposition methods for solving large-scale optimization problems, especially those arising in interconnected infrastructure systems. Several factors (e.g., the Internet-of-Things) are driving infrastructure systems to become more interdependent. As a result, these complex systems are increasingly exposed to a variety of risks and demand elaborate optimization modeling that allows risk-informed decision-making. The resulting optimization models, however, are often of large-scale and have computationally challenging properties. In this regard, this thesis studies how to formulate optimization models mitigating their risks and develop decomposition methods for solving these models with improved computational properties. We first present a network planning problem for electricity distribution grids and their associated communication networks. The problem is formulated as a two-stage mixed-integer linear program and is of large-scale, since it captures hundreds of potential disaster scenarios as well as grids’ dependencies on the communication systems. To deal with its vast size, we develop a branch-and-price algorithm that features a tight lower bound and various acceleration schemes that address degeneracy. The model and algorithm were evaluated on a variety of test cases, the results of which demonstrate the impact of the risk- aware planning decisions as well as the computational benefits of the proposed solution approach. Next, we propose a unit scheduling problem of electric grids. We introduce gas network awareness into the scheduling problem to alleviate risks from natural gas networks. The resulting optimization model is formulated as a bi-level optimization problem. To address inherent computational challenges in solving bilevel problems, we develop a dedicated Benders decomposition method for solving a certain class of bilevel problems (discrete-continuous bilevel problems), which subsumes the proposed model. The algorithm features a Benders subproblem decomposition technique that breaks down the Benders subproblem into two more tractable problems. We test the model and the solution approach on a practically-relevant network data set. The results demonstrate that the risk-aware opera- tional decision is instrumental in avoiding disruptions caused by gas system insecurity. It is also demonstrated that the proposed decomposition algorithm not only improves the computational performance of existing solution methods but also allows intuitive interpretation of Benders cuts.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155075/1/gbyeon_1.pd