Participation of distributed loads in power markets that co-optimize energy and reserves

Thesis (Ph.D.)--Boston UniversityAs the integration of Renewable Generation into today's Power Systems is progressing rapidly, capacity reserve requirements needed to compensate for the intermittency of renewable generation is increasing equally rapidly. A major objective of this thesis is to promote the affordability of incremental reserves by enabling loads to provide them through demand response. Regulation Service (RS) reserves, a critical type of bi-directional Capacity Reserves, are provided today by expensive and environmentally unfriendly centralized fossil fuel generators. In contrast, we investigate the provision of low-cost RS reserves by the demand-side. This is a challenging undertaking since loads must first promise reserves in the Hour Ahead Markets, and then be capable of responding to the dynamic ISO signals by adjusting their consumption effectively and efficiently. To this end, we use Stochastic Control, Optimization Theory, and Approximate Dynamic Programming to develop a decision support framework that assists Smart Neighborhood Operators or Smart Building Operators (SNOs/SBOs) to become demand-side-providers of RS reserve. We first address the SNO/SBO short time scale operational task of responding to the Independent System Operator's (ISO) dynamic RS requests. We start by developing a model-based Markovian decision problem that trades off ISO RS tracking against demand response related utility loss. Starting with a model based approach we obtain near optimal operational policies through a novel approximate policy iteration technique and an actor critic approach which is robust to partial knowledge of the underlying system dynamics. We then abandon the model based terrain and solve the dynamic operational problem through reinforcement learning that is capable of modeling a population of duty cycle appliances with realistic thermodynamics. We finally propose a smart thermostat design and develop an adaptive control policy that can drive the smart thermostat effectively. The latter approach is particularly suited for systems whose dynamics and dynamically changing consumer preferences are not known or observed beyond the total power consumption. We then address the SNO/SBO task of bidding RS reserves to the hour ahead market. This task determines the maximal RS reserves that the SNO/SBO can promise based on information available at the beginning of an hour, so as to maximize the associated hour-ahead revenues minus the expected average operating cost that will be incurred during the operational task to follow. To accomplish this task, we (i) develop probabilistic constraints that model the feasible maximum reserves which can be offered to the market without exceeding the SNO/SBO's ability to later track the unanticipated dynamic ISO RS signal, and (ii) calibrate a describing function that approximates the average operational cost as a function of the maximal reserves that can be feasibly offered in the day ahead market. The above is made possible by statistical analysis of the controlled system's stochastic dynamics and properties of the optimal dynamic policies that we derive. The contribution of the thesis is twofold: The solution of a difficult stochastic control problem that is crucial for effective demand-response-based provision of regulation service, and, the characterization of key properties of the stochastic control problem solution, which allow its integration into the hour-ahead market bidding problem

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Selection of return channels and recovery options for used products

Due to legal, economic and socio-environmental factors, reverse logistics practices and extended producer responsibility have developed into a necessity in many countries. The end results and expectations may differ, but the motivation remains the same. Two significant components in a reverse logistics system -product recovery options and return channels - are the focus of this thesis. The two main issues examined are allocation of the returned products to recovery options, and selection of the collection methods for product returns. The initial segment of this thesis involves the formulation of a linear programming model to determine the optimal allocation of returned products differing in quality to specific recovery options. This model paves the way for a study on the effects of flexibility on product recovery allocation. A computational example utilising experimental data was presented to demonstrate the viability of the proposed model. The results revealed that in comparison to a fixed match between product qualities and recovery options, the product recovery operation appeared to be more profitable with a flexible allocation. The second segment of this thesis addresses the methods employed for the initial collection of returned products. A mixed integer nonlinear programming model was developed to facilitate the selection of optimal collection methods for these products. This integrated model takes three different initial collection methods into consideration. The model is used to solve an illustrative example optimally. However, as the complexity of the issue renders this process ineffective in the face of larger problems, the Lagrangian relaxation method was proposed to generate feasible solutions within reasonable computational times. This method was put to the test and the results were found to be encouraging

Resource selection and route generation in discrete manufacturing environment

When put to various sources, the question of which sequence of operations and machines is best for producing a particular component will often receive a wide range of answers. When the factors of optimum cutting conditions, minimum time, minimum cost, and uniform equipment utilisation are added to the equation, the range of answers becomes even more extensive. Many of these answers will be 'correct', however only one can be the best or optimum solution. When a process planner chooses a route and the accompanying machining conditions for a job, he will often rely on his experience to make the choice. Clearly, a manual generation of routes does not take all the important considerations into account. The planner may not be aware of all the factors and routes available to him. A large workshop might have hundreds of possible routes, even if he did know it all', he will never be able to go through all the routes and calculate accurately which is the most suitable for each process - to do this, something faster is required. This thesis describes the design and implementation of an Intelligent Route Generator. The aim is to provide the planner with accurate calculations of all possible production routes m a factory. This will lead up to the selection of an optimum solution according to minimum cost and time. The ultimate goal will be the generation of fast decisions based on expert information. Background knowledge of machining processes and machine tools was initially required, followed by an identification of the role of the knowledge base and the database within the system. An expert system builder. Crystal, and a database software package, DBase III Plus, were chosen for the project. Recommendations for possible expansion of and improvements to the expert system have been suggested for future development

Framework for Managing an Efficient and Effective Pharmaceutical Supply Chain in Malaysia

The pressure in pharmaceutical sectors in Malaysia is increasing as the country new policy, in regards to medical care, is to standardise the existing Good Manufacturing Practise (GMP) and Good Distribution Practise (GDP) guidelines, disseminate the medical information and evaluate the pharmaceutical products, implement knowledge transfer when it comes to public service. In line with this effort, Ministry of Health (MOH) Malaysia is investing into efficient implementation of GMP and GDP to assure the impact of drug quality are contemplated. However, it is unclear if all the partners/stakeholders within this process are aware about the appropriate indications and possible limitations. In addition, many organisations uses a wide variety of metrics to measure their performance, typically in two broad categories efficiency and effectiveness to improve its customer service which is crucial in the pharmaceutical industry. Efficiency metrics such as inventory costs, operations cost, and utilisation of resources are broader in scope but not linked to the strategic objectives of the organisations. Effectiveness metrics such as customer satisfaction and total supply chain costs represent significant leap in integration, visibility and alignment with overall supply chain performance. Therefore, main aim of this research are to design and develop an integrated framework involving efficiency, effectiveness, optimisation, and GDP dimensions to support the design of pharmaceutical cold supply chain in Malaysia. In addition, the philosophical approach used in this study and process of developing a supply chain management framework will be justified. Then the potential supply chain frameworks and models available and are widely implemented in the industry will be evaluated. This framework has been developed by integrating six models that are widely implemented by companies in various industry namely, Strategic Fit model to explain the strategic role and objective of the framework, Good Distribution Practise (GDP) model to clarify the supply chain specifications and requirements, Total Quality Management (TQM) and Quality Risk Management to establish all processes that are designed encompass quality assurance and continuous improvements, Supply Chain Network Optimisation model to ensure the optimal distribution pattern has been achieved, and lastly performance indicator model to measure efficiency and effectiveness. The framework has been validated and refined through the feedback received from industry. To conclude, effective GDP implementation in its operations may improve their efficiency, effectiveness and optimisation, and may experience reduction in costs and increase in customer and employee satisfaction

Railway Crew Rescheduling: Novel approaches and extensions

Passenger railway operators meticulously plan how to use the rolling stock and the crew in order to operate the published timetable. However, unexpected events such as infrastructure malfunctions, or weather conditions disturb the operation every day. As a consequence, significant changes, such as cancellation of trains, to the timetable must be made. If these timetable changes make the planned rolling stock and crew schedule infeasible, one speaks of a disruption. It is very important that these schedules are fixed such that no additional cancellations of trains are necessary. Nowadays this rescheduling is still done manually by the dispatchers in the control centers. In this thesis we use Operations Research techniques to develop solution approaches for crew rescheduling during disruptions. This enables us to solve the basic operational crew rescheduling problem in a short amount of computation time. Moreover, we studied an extension to the basic problem where the departure times of some trains may be delayed by some minutes. We show that this can lead to significantly better solutions for some real-life instances. Furthermore, we presented two new quasi robust optimization approaches that deal with the uncertainty in the length of the disruption. The computational study reveals that one of these approaches outperforms a naive approach in many cases. We believe that the methods developed in this thesis provided the foundation for a decision support system for railway crew rescheduling

Evaluating Network Analysis and Agent Based Modeling for Investigating the Stability of Commercial Air Carrier Schedules

For a number of years, the United States Federal Government has been formulating the Next Generation Air Transportation System plans for National Airspace System improvement. These improvements attempt to address air transportation holistically, but often address individual improvements in one arena such as ground or in-flight equipment. In fact, air transportation system designers have had only limited success using traditional Operations Research and parametric modeling approaches in their analyses of innovative operations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be deployed with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed