The congested multicommodity network design problem

This paper studies a version of the fixed-charge multicommodity network design problem where in addition to the traditional costs of flow and design, congestion at nodes is explicitly considered. The problem is initially modeled as a nonlinear integer programming formulation and two solution approaches are proposed: (i) a reformulation of the problem as a mixed integer second order cone program to optimally solve the problem for small to medium scale problem instances, and (ii) an evolutionary algorithm using elements of iterated local search and scatter search to provide upper bounds. Extensive computational results on new benchmark problem instances and on real case data are presented

Interdependence between transportation system and power distribution system: a comprehensive review on models and applications

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.fi=vertaisarvioitu|en=peerReviewed

Mathematical Optimization for Routing and Logistic Problems

In this thesis, we focus on mathematical optimization models and algorithms for solving routing and logistic problems. The first contribution regards a path and mission planning problem, called Carrier-Vehicle Traveling Salesman Problem (CVTSP), for a system of heterogeneous vehicles. A Mixed-Integer Second Order Conic Programming (MISOCP) model and a Benders-like enumeration algorithm are presented for solving CVTSP. The second work concerns a class of routing problems, referred to as Interceptor Vehicle Routing Problems (IVRPs). They generalize VRPs in the sense that target points are allowed to move from their initial location according to a known motion. We present a novel MISOCP formulation and a Branch-and-Price algorithm based on a Lagrangian Relaxation of the vehicle-assignment constraints. Other two contributions focus on waste flow management problems: the former considers a deterministic setting in which a Mixed-Integer Linear Programming (MILP) formulation is used as a Decision Support System for a real-world waste operator, whereas the latter deals with the uncertainty of the waste generation amounts by means of Two-Stage Multiperiod Stochastic Mixed-Integer Programming formulations. Finally, we give an overview on the optimization challenges arising in electric car-sharing systems, both at strategic and tactical planning level

Finding an energy efficient path for plug-in electric vehicles with speed optimization and travel time restrictions

Transportation is one of the main factors when global total energy consumption is considered and is a significant contributor to emissions of harmful gases including carbon dioxide (CO2). Due to their lower tailpipe CO2 emissions compared to the vehicles with internal combustion engines, electric vehicles provide an opportunity to reduce environmental impacts of transportation. In this direction, a problem for plug-in electric vehicles (PEVs) is studied where the aim is to find an energy efficient path. Given an origin–destination pair over a directed network, this problem involves determining a path joining origin and destination, the speed of the PEV on each road segment, i.e., arc, along the path, the charging stations the PEV will stop by, and how much to recharge at each stop so as to minimize the total amount energy consumption. There are speed limits on each road segment, and PEV has to arrive at the destination on or before a given total time limit. For this problem, firstly, a mixed-integer second order cone programming formulation (MISOCP) is proposed. Secondly, to be able to solve larger size instances, a matheuristic is developed. Lastly, an iterated local search (ILS) algorithm is designed for this problem. Solution quality and computation times of the heuristics and the exact algorithm are compared on different instances. Differently from the literature, the speed values of the PEV on the arcs are considered as continuous decision variables in all proposed solution approaches. Moreover, consideration of the speed limits which can be legal limits or limits imposed by congestion makes our problem more realistic. The analysis of the results of the computational experiments gives the user an insight to select the proper solution approach based on the instance settings. MISOCP formulation becomes inadequate for larger instances. On the other hand, the heuristic solution approaches can solve such instances within reasonable computational times and therefore they have the potential to be integrated in some software to dynamically find energy efficient paths.</p

Finding an energy efficient path for plug-in electric vehicles with speed optimization and travel time restrictions

Transportation is one of the main factors when global total energy consumption is considered and is a significant contributor to emissions of harmful gases including carbon dioxide (CO2). Due to their lower tailpipe CO2 emissions compared to the vehicles with internal combustion engines, electric vehicles provide an opportunity to reduce environmental impacts of transportation. In this direction, a problem for plug-in electric vehicles (PEVs) is studied where the aim is to find an energy efficient path. Given an origin–destination pair over a directed network, this problem involves determining a path joining origin and destination, the speed of the PEV on each road segment, i.e., arc, along the path, the charging stations the PEV will stop by, and how much to recharge at each stop so as to minimize the total amount energy consumption. There are speed limits on each road segment, and PEV has to arrive at the destination on or before a given total time limit. For this problem, firstly, a mixed-integer second order cone programming formulation (MISOCP) is proposed. Secondly, to be able to solve larger size instances, a matheuristic is developed. Lastly, an iterated local search (ILS) algorithm is designed for this problem. Solution quality and computation times of the heuristics and the exact algorithm are compared on different instances. Differently from the literature, the speed values of the PEV on the arcs are considered as continuous decision variables in all proposed solution approaches. Moreover, consideration of the speed limits which can be legal limits or limits imposed by congestion makes our problem more realistic. The analysis of the results of the computational experiments gives the user an insight to select the proper solution approach based on the instance settings. MISOCP formulation becomes inadequate for larger instances. On the other hand, the heuristic solution approaches can solve such instances within reasonable computational times and therefore they have the potential to be integrated in some software to dynamically find energy efficient paths.</p

Latency and Reliability Aware Edge Computation Offloading in 5G Networks

Empowered by recent technological advances and driven by the ever-growing population density and needs, the conception of 5G has opened up the expectations of what mobile networks are capable of to heights never seen before, promising to unleash a myriad of new business practices and paving the way for a surging number of user equipments to carry out novel service operations. The advent of 5G and networks beyond will hence enable the vision of Internet of Things (IoT) and smart city with its ubiquitous and heterogeneous use cases belonging to various verticals operating on a common underlying infrastructure, such as smart healthcare, autonomous driving, and smart manufacturing, while imposing extreme unprecedented Quality of Service (QoS) requirements in terms of latency and reliability among others. Due to the necessity of those modern services such as traffic coordination, industrial processes, and mission critical applications to perform heavy workload computations on the collected input, IoT devices such as cameras, sensors, and Cyber-Physical Systems (CPSs), which have limited energy and processing capabilities are put under an unusual strain to seamlessly carry out the required service computations. While offloading the devices' workload to cloud data centers with Mobile Cloud Computing (MCC) remains a possible alternative which also brings about a high computation reliability, the latency incurred from this approach would prevent from satisfying the services' QoS requirements, in addition to elevating the load in the network core and backhaul, rendering MCC an inadequate solution for handling the 5G services' required computations. In light of this development, Multi-access Edge Computing (MEC) has been proposed as a cutting edge technology for realizing a low-latency computation offloading by bringing the cloud to the vicinity of end-user devices as processing units co-located within base stations leveraging the virtualization technique. Although it promises to satisfy the stringent latency service requirements, realizing the edge-cloud solution is coupled with various challenges, such as the edge servers' restricted capacity, their reduced processing reliability, the IoT devices' limited offloading energy, the wireless offloading channels' often weak quality, the difficulty to adapt to dynamic environment changes and to under-served networks, and the Network Operators (NOs)' cost-efficiency concerns. In light of those conditions, the NOs are consequently looking to devise efficient innovative computation offloading schemes through leveraging novel technologies and architectures for guaranteeing the seamless provisioning of modern services with their stringent latency and reliability QoS requirements, while ensuring the effective utilization of the various network and devices' available resources. Leveraging a hierarchical arrangement of MEC with second-tier edge servers co-located within aggregation nodes and macro-cells can expand the edge network's capability, while utilizing Unmanned Aerial Vehicles (UAVs) to provision the MEC service via UAV-mounted cloudlets can increase the availability, flexibility, and scalability of the computation offloading solution. Moreover, aiding the MEC system with UAVs and Intelligent Reflecting Surfaces (IRSs) can improve the computation offloading performance by enhancing the wireless communication channels' conditions. By effectively leveraging those novel technologies while tackling their challenges, the edge-cloud paradigm will bring about a tremendous advancement to 5G networks and beyond, opening the door to enabling all sorts of modern and futuristic services. In this dissertation, we attempt to address key challenges linked to realizing the vision of a low-latency and high-reliability edge computation offloading in modern networks while exploring the aid of multiple 5G network technologies. Towards that end, we provide novel contributions related to the allocation of network and devices' resources as well as the optimization of other offloading parameters, and thereby efficiently utilizing the underlying infrastructure such as to enable energy and cost-efficient computation offloading schemes, by leveraging several customized solutions and optimization techniques. In particular, we first tackle the computation offloading problem considering a multi-tier MEC with a deployed second-tier edge-cloud, where we optimize its use through proposed low-complexity algorithms, such as to achieve an energy and cost-efficient solution that guarantees the services' latency requirements. Due to the significant advantage of operating MEC in heterogeneous networks, we extend the scenario to a network of small-cells with the second-tier edge server being co-located within the macro-cell which can be reached through a wireless backhaul, where we optimize the macro-cell server use along with the other offloading parameters through a proposed customized algorithm based on the Successive Convex Approximation (SCA) technique. Then, given the UAVs' considerable ability in expanding the capabilities of cellular networks and MEC systems, we study the latency and reliability aware optimized positioning and use of UAV-mounted cloudlets for computation offloading through two planning and operational problems while considering tasks redundancy, and propose customized solutions for solving those problems. Finally, given the IRSs' ability to also enhance the channel conditions through the tuning of their passive reflecting elements, we extend the latency and reliability aware study to a scenario of an IRS-aided MEC system considering both a single-user and multi-user OFDMA cases, where we explore the optimized IRSs' use in order to reveal their role in reducing the UEs' offloading consumption energy and saving the network resources, through proposed customized solutions based on the SCA approach and the SDR technique

Optimal speed trajectory and energy management control for connected and automated vehicles

Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces

Exact and Heuristic Algorithms for the Carrier-Vehicle Traveling Salesman Problem

This paper presents new structural properties for the Carrier-Vehicle Traveling Salesman Problem. The authors provide a new mixed integer second order conic optimization formulation, with associated optimality cuts based on the structural properties, and an Iterated Local Search (ILS) algorithm. Computational experiments on instances from the literature demonstrate the superiority of the new formulation to the existing models and algorithms in the literature, and the high quality solutions found by the ILS algorithm

Optimization of energy-constrained resources in radial distribution networks with solar PV

The research objective of the proposed dissertation is to make best use of available distributed energy resources to meet dynamic market opportunities while accounting for AC physics of unbalanced distribution networks and the uncertainty of distributed solar photovoltaics (PV). With ever increasing levels of renewable generation, distribution system operations must shift from a mindset of static unidirectional power flows to dynamic, unpredictable bidirectional flows. To manage this variability, distributed energy resources (DERs; e.g.,solar PV inverters, inverter-based batteries, electric vehicles, water heaters, A/Cs) need to be coordinated for reliable and resilient operation. This introduces the challenge of coordinating such resources at scale and within confines of the existing distribution system. It also becomes important to develop efficient and accurate models of the distribution system to achieve desired operating objectives such as tracking a market reference, reduction in operation cost or voltage regulation. This work surveys, discusses the challenges and proposes solutions to the modeling and optimization of realistic distribution systems with significant penetration of renewables and controllable DERs, including energy storage. To contain this increase in system complexity as result of the large number of controllable DERs available, the distribution system has to be adapted from a passive Volt-Var focused operator to a more active manager of resources. To approach this challenge, in this work, we propose two main approaches. The first is a utility centric approach, where the utility controls the dispatch of flexible resources based on solving an optimization problem. This approach would require the utility to have all the network and resource data and also the control over customer devices. Another approach is a more aggregator centric approach, where an aggregator is an entity that represents an aggregation of many diverse DERs or a Virtual Battery (VB). In this approach, it is the role of the aggregator to dispatch DERs, whereas the utility provides certain bounds and limits (calculated offline), which the aggregator (which dispatches resources in real-time) must operate under. The benefits of such an approach lie in improved data-privacy and real-time dispatch. We present simulation results validating the proposed methods on various standard IEEE and realistic distribution feeders

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG