Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities

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

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

Flexibility procurement by EVs in a Danish active distribution network: Study cases from the island of Bornholm

Modello per generare diversi charging patterns dei veicoli elettrici basato su dati reali. Implementazione ed analisi di due reti di bassa tensione danesi con diverse caratteristiche di carico. Analisi delle due reti con l'integrazione passiva ed attiva dei veicoli elettrici con diversi livelli di carica e penetrazione nel sistema. Analisi economica basata sull'utilizzo dei veicoli come componenti attivi del sistema per evitare sovraccarichi e problemi di bassa tensione