On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165

Evaluating the New Greek Electricity Market Rules

The Greek Regulatory Authority for Energy (RAE), in view of the initiation of the new wholesale electricity market on January 1st 2009 as a Day-Ahead mandatory pool, undertook the design and implementation of a simulator for the market. The simulator consists of several interacting modules representing all key market operations and dynamics including day-ahead scheduling, natural gas system constraints, unplanned variability of loads and available capacity driven either by uncertain stochastic outcomes or deliberate participant schedule deviations, real time dispatch, and financial settlement of day ahead and real-time schedule differences. The modules are integrated into one software package. The intended use of the simulator is to elaborate on and allow RAE to investigate the impact of participant decision strategies on market outcomes. The ultimate purpose is to evaluate the effectiveness of Market Rules, whether existing or contemplated, in providing incentives for competitive behaviour and in discouraging gaming and market manipulation. In this paper the simulator is used to analyze market design aspects and rules concerning the co-optimization of energy and reserves in the Day-Ahead energy market and the efficiency of the imbalance settlement procedure compared to real-time pricing.Electricity Market Design; Market Simulation; Regulation; Unit Commitment

Business model requirements and challenges in the mobile telecommunication sector

The telecommunications business is undergoing a critical revolution, driven by innovative technologies, globalization, and deregulation. Cellular networks and telecommunications bring radical changes to the way telecom businesses are conducted. Globalization, on the other hand, is tearing down legacy barriers and forcing monopolistic national carriers to compete internationally. Moreover, the noticeable progress of many countries towards deregulation coupled with liberalization is significantly increasing telecom market power and allowing severe competition. The implications of this transition have changed the business rules of the telecom industry. In addition, entrants into the cellular industry have had severe difficulties due to inexistent or weak Business Models (BMs). Designing a BM for a mobile network operator is complex and requires multiple actors to balance different and often conflicting design requirements. Hence, there is a need to enhance operators’ ability in determining what constitutes the most viable business model to meet their strategic objectives within this turbulent environment. In this paper, the authors identify the main mobile BM dimensions along with their interdependencies and further analysis provides mobile network operators with insights to improve their business models in this new ‘boundary-less’ landscape

Linear Optimal Power Flow Using Cycle Flows

Linear optimal power flow (LOPF) algorithms use a linearization of the alternating current (AC) load flow equations to optimize generator dispatch in a network subject to the loading constraints of the network branches. Common algorithms use the voltage angles at the buses as optimization variables, but alternatives can be computationally advantageous. In this article we provide a review of existing methods and describe a new formulation that expresses the loading constraints directly in terms of the flows themselves, using a decomposition of the network graph into a spanning tree and closed cycles. We provide a comprehensive study of the computational performance of the various formulations, in settings that include computationally challenging applications such as multi-period LOPF with storage dispatch and generation capacity expansion. We show that the new formulation of the LOPF solves up to 7 times faster than the angle formulation using a commercial linear programming solver, while another existing cycle-based formulation solves up to 20 times faster, with an average speed-up of factor 3 for the standard networks considered here. If generation capacities are also optimized, the average speed-up rises to a factor of 12, reaching up to factor 213 in a particular instance. The speed-up is largest for networks with many buses and decentral generators throughout the network, which is highly relevant given the rise of distributed renewable generation and the computational challenge of operation and planning in such networks.Comment: 11 pages, 5 figures; version 2 includes results for generation capacity optimization; version 3 is the final accepted journal versio

Broadband Technologies on Residential Acces

The diffusion of broadband technologies is a hot topic for developed and many developing countries. Although the provision of service has many similar aspects, the overall and specific penetration of broadband technologies varies significantly in these countries. This study aims to examine the place of users' perceptions in the broadband issue by studying the development of the selected technologies and national policies in the light of the general information technology diffusion aspects.Diffusion of Technology, Broadband Technologies, Development of National Policies and Pricing Issues

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems