Multistage Expansion Planning of Active Distribution Systems: Towards Network Integration of Distributed Energy Resources

Over the last few years, driven by several technical and environmental factors, there has been a growing interest in the concept of active distribution networks (ADNs). Based on this new concept, traditional passive distribution networks will evolve into modern active ones by employing distributed energy resources (DERs) such as distributed generators (DGs), energy storage systems (ESSs), and demand responsive loads (DRLs). Such a transition from passive to active networks poses serious challenges to distribution system planners. On the one hand, the ability of DGs to directly inject active and reactive powers into the system nodes leads to bidirectional power flows through the distribution feeders. This issue, if not adequately addressed at the design stage, can adversely affect various operational aspects of ADNs, specifically the reactive power balance and voltage regulation. Therefore, the new context where DGs come into play necessitates the development of a planning methodology which incorporates an accurate network model reflecting realistic operational characteristics of the system. On the other hand, large-scale integration of renewable DGs results in the intermittent and highly volatile nodal power injections and the implementation of demand response programs further complicates the long-term predictability of the load growth. These factors introduce a tremendous amount of uncertainty to the planning process of ADNs. As a result, effective approaches must also be devised to properly model the major sources of uncertainty. Based on the above discussion, successful transition from traditional passive distribution networks to modern active ones requires a planning methodology that firstly includes an accurate network model, and secondly accounts for the major sources of uncertainty. However, incorporating these two features into the planning process of ADNs is a very complex task and requires sophisticated mathematical programming techniques that are not currently available in the literature. Therefore, this research project aim to develop a comprehensive planning methodology for ADNs, which is capable of dealing with different types of DERs (i.e., DGs, ESSs, and DRLs), while giving full consideration to the above-mentioned two key features. To achieve this objective, five major steps are defined for the project. Step 1 develops a deterministic mixed-integer linear programming (MILP) model for integrated expansion planning of distribution network and renewable/conventional DGs, which includes a highly accurate network model based on a linear format of AC power flow equations. This MILP model can be solved using standard off-the-shelf mathematical programming solvers that not only guarantee convergence to the global optimal solution, but also provide a measure of the distance to the global optimum during the solution process. Step 2 proposes a distributionally robust chance-constrained programming approach to characterize the inherent uncertainties of renewable DGs and loads. The key advantage of this approach is that it requires limited information about the uncertain parameters, rather than perfect knowledge of their probability distribution functions. Step 3 devises a fast Benders decomposition-based solution procedure that paves the way for effective incorporation of ESSs and DRLs into the developed planning methodology. To this end, two effective acceleration strategies are proposed to significantly enhance the computational performance of the classical Benders decomposition algorithm. Eventually, Steps 4 and 5 propose appropriate models for ESSs and DRLs and integrate them into the developed planning methodology. In this regard, a sequential-time power flow simulation method is also proposed to incorporate the short-term operation analysis of ADNs into their long-term planning studies. By completing the above-defined steps, the planning model developed in Step 1 will be gradually evolved, so that Step 5 will yield the final comprehensive planning methodology for ADNs

Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging

In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, the power grid to ensure our energy supply, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play. Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above, and many more. It exploits and orchestrates i.a. techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, optimization, stochastics, statistics, decision making under uncertainty, machine learning, as well as calculus, combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.Comment: Habilitation Thesi

Resource Management in Constrained Dynamic Situations

Resource management is considered in this dissertation for systems with limited resources, possibly combined with other system constraints, in unpredictably dynamic environments. Resources may represent fuel, power, capabilities, energy, and so on. Resource management is important for many practical systems; usually, resources are limited, and their use must be optimized. Furthermore, systems are often constrained, and constraints must be satisfied for safe operation. Simplistic resource management can result in poor use of resources and failure of the system. Furthermore, many real-world situations involve dynamic environments. Many traditional problems are formulated based on the assumptions of given probabilities or perfect knowledge of future events. However, in many cases, the future is completely unknown, and information on or probabilities about future events are not available. In other words, we operate in unpredictably dynamic situations. Thus, a method is needed to handle dynamic situations without knowledge of the future, but few formal methods have been developed to address them. Thus, the goal is to design resource management methods for constrained systems, with limited resources, in unpredictably dynamic environments. To this end, resource management is organized hierarchically into two levels: 1) planning, and 2) control. In the planning level, the set of tasks to be performed is scheduled based on limited resources to maximize resource usage in unpredictably dynamic environments. In the control level, the system controller is designed to follow the schedule by considering all the system constraints for safe and efficient operation. Consequently, this dissertation is mainly divided into two parts: 1) planning level design, based on finite state machines, and 2) control level methods, based on model predictive control. We define a recomposable restricted finite state machine to handle limited resource situations and unpredictably dynamic environments for the planning level. To obtain a policy, dynamic programing is applied, and to obtain a solution, limited breadth-first search is applied to the recomposable restricted finite state machine. A multi-function phased array radar resource management problem and an unmanned aerial vehicle patrolling problem are treated using recomposable restricted finite state machines. Then, we use model predictive control for the control level, because it allows constraint handling and setpoint tracking for the schedule. An aircraft power system management problem is treated that aims to develop an integrated control system for an aircraft gas turbine engine and electrical power system using rate-based model predictive control. Our results indicate that at the planning level, limited breadth-first search for recomposable restricted finite state machines generates good scheduling solutions in limited resource situations and unpredictably dynamic environments. The importance of cooperation in the planning level is also verified. At the control level, a rate-based model predictive controller allows good schedule tracking and safe operations. The importance of considering the system constraints and interactions between the subsystems is indicated. For the best resource management in constrained dynamic situations, the planning level and the control level need to be considered together.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137128/1/sjinu_1.pd

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Construction IT in 2030: a scenario planning approach

Summary: This paper presents a scenario planning effort carried out in order to identify the possible futures that construction industry and construction IT might face. The paper provides a review of previous research in the area and introduces the scenario planning approach. It then describes the adopted research methodology. The driving forces of change and main trends, issues and factors determined by focusing on factors related to society, technology, environment, economy and politics are discussed. Four future scenarios developed for the year 2030 are described. These scenarios start from the global view and present the images of the future world. They then focus on the construction industry and the ICT implications. Finally, the preferred scenario determined by the participants of a prospective workshop is presented

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Harmonised Principles for Public Participation in Quality Assurance of Integrated Water Resources Modelling

The main purpose of public participation in integrated water resources modelling is to improve decision-making by ensuring that decisions are soundly based on shared knowledge, experience and scientific evidence. The present paper describes stakeholder involvement in the modelling process. The point of departure is the guidelines for quality assurance for `scientific` water resources modelling developed under the EU research project HarmoniQuA, which has developed a computer based Modelling Support Tool (MoST) to provide a user-friendly guidance and a quality assurance framework that aim for enhancing the credibility of river basin modelling. MoST prescribes interaction, which is a form of participation above consultation but below engagement of stakeholders and the public in the early phases of the modelling cycle and under review tasks throughout the process. MoST is a flexible tool which supports different types of users and facilitates interaction between modeller, manager and stakeholders. The perspective of using MoST for engagement of stakeholders e.g. higher level participation throughout the modelling process as part of integrated water resource management is evaluate

Operationalizing the circular city model for naples' city-port: A hybrid development strategy

The city-port context involves a decisive reality for the economic development of territories and nations, capable of significantly influencing the conditions of well-being and quality of life, and of making the Circular City Model (CCM) operational, preserving and enhancing seas and marine resources in a sustainable way. This can be achieved through the construction of appropriate production and consumption models, with attention to relations with the urban and territorial system. This paper presents an adaptive decision-making process for Naples (Italy) commercial port's development strategies, aimed at re-establishing a sustainable city-port relationship and making Circular Economy (CE) principles operative. The approach has aimed at implementing a CCM by operationalizing European recommendations provided within both the Sustainable Development Goals (SDGs) framework-specifically focusing on goals 9, 11 and 12-and the Maritime Spatial Planning European Directive 2014/89, to face conflicts about the overlapping areas of the city-port through multidimensional evaluations' principles and tools. In this perspective, a four-step methodological framework has been structured applying a place-based approach with mixed evaluation methods, eliciting soft and hard knowledge domains, which have been expressed and assessed by a core set of Sustainability Indicators (SI), linked to SDGs. The contribution outcomes have been centred on the assessment of three design alternatives for the East Naples port and the development of a hybrid regeneration scenario consistent with CE and sustainability principles. The structured decision-making process has allowed us to test how an adaptive approach can expand the knowledge base underpinning policy design and decisions to achieve better outcomes and cultivate a broad civic and technical engagement, that can enhance the legitimacy and transparency of policies

GIS-based approach for assessing the energy potential and the financial feasibility of run-off-river hydro-power in Alpine valleys

In the last decade, European attractive policies are favoring the construction of new run-off hydro-power plants. The realization cost of these plants is quite low in mountain areas thanks to small water discharges and high gross heads. For this reason, small rivers have been strongly exploited without considering an optimal use of the resource. Nowadays, available sites are often in areas with low accessibility and a greater specific cost of civil engineering works. However, during the planning of new small hydro-power plants, the dependency of physical, technical, legal and financial variable on space is often not assessed. The tool presented in this paper addresses this gap to support the planning of run-off-river plants. The method improves on previous approaches by (1) integrating all the legal, technical and financial analysis in a GIS tool, and (2) trying to validate the site-specific model with local knowledge. The tool is applied to the Gesso and Vermenagna valleys in the Alps. Information and data were collected and discussed with local stakeholders in order to improve the model results