Optimization methods for developing electric vehicle charging strategies

Electric vehicles (EVs) are considered to be a crucial and proactive player in the future for transport electrification, energy transition, and emission reduction, as promoted by policy-makers, relevant industries, and the academia. EV charging would account for a non-negligible share in the future electricity demand. The integration of EV brings both challenges and opportunities to the electricity system, mainly from their charging profiles. When EV charging behaviors are uncontrolled, their potentially high charging rate and synchronous charging patterns may result in the bottleneck of the grid capacity and the shortage of generation ramping capacity. However, the promising load shifting potential of EVs can alleviate these problems and even bring additional flexibilities to the demand side for further applications, such as peak shaving and the integration of renewable energy. To grasp these opportunities, novel controlled charging strategies should be developed to help integrate electric vehicles into energy systems. However, corresponding methods in current literature often have customized assumptions or settings so that they might not be practically or widely applied. Furthermore, the attention of literature is more paid to explaining the results of the methods or making consequent policy recommendations, but not sufficiently paid to demonstrating the methods themselves. The lack of the latter might undermine the credibility of the work and hinder readers’ understanding. Therefore, this thesis serves as a methodological framework in response to the fundamental and universal challenges in developing charging strategies for integrating EV into energy systems. The discussions aim to raise readers’ awareness of the essential but often unnoticed concerns in model development and hopefully would enlighten future researchers into this topic. Specifically, this cumulative thesis comprises four papers and analyzes the research topic from two perspectives. With Paper A and Paper B, the micro perspective of the thesis is more applied and focuses on the successful implementation of charging scheduling solutions for each EV individually. Paper A proposes a two-stage scenario-based stochastic linear programming model to schedule EV charging behaviors and considers the uncertainties from future EVs. The model is calculated in a rolling window fashion with updated parameters. Scenario generation for future EVs is simulated by inhomogeneous Markov chains, and scenario reduction is achieved by a fast forward selection method to reduce the computational burden. The objective function is formulated as variance minimization so that the model can be flexibly implemented for various applications. Paper B applies the model proposed in Paper A to investigate how the generation of a wind turbine could be correlated with the EV controlled charging demand. An empirical controlled charging strategy is designed for comparison where EVs would charge as much as possible when wind generation is sufficient or would postpone charging otherwise. Although these two controlled charging strategies perform similarly in terms of wind energy utilization, the solutions from the proposed model could additionally alleviate the volatility of wind energy generation by matching the EV charging curve to the electricity generation profile. With Paper C and Paper D, the macro perspective of the thesis is more explorative and investigates how EVs as a whole would contribute to energy transition or emission reduction. Paper C investigates the greenhouse gas emissions of EVs under different charging strategies in Europe in 2050. Methodologically, the paper proposes an EV module that enables different EV controlled charging strategies to be endogenously determined by energy system models. The paper concludes that EVs would contribute to a 36% emission reduction on the European level even under an uncontrolled charging strategy. Unidirectional and bidirectional controlled charging strategies could further reduce emissions by 4% and 11%, respectively, compared with the original level. As a follow-up study of Paper C, Paper D develops, demonstrates, improves, and compares three different types of EV aggregation methods for integrating an EV module into energy system models. The analysis and demonstration of these methods are achieved by having a simplified energy system model as a testbed and the results from the individual EV modeling method as the benchmark. As different EV aggregation methods share the same data set as for the individual EV modeling method, the disturbance from parameters is minimized, and the influence from mathematical formulations is highlighted. These EV aggregation methods are compared from multiple aspects

Measure-valued diffusions and stochastic equations with Poisson process

Suppose that we are given a locally compact metric space E. Let C(E) denote the set of bounded continuous functions on E, and C0(E) its subset of continuous functions vanishing at infinity. The subsets of non-negative elements of C(E) and C0(E) are denoted respectively by C+(E) and C+0 (E). Let (Pt)t≥0 be a strongly continuous conservativ

Three Essays on Credit Market

My dissertation focuses primarily on credit markets from a corporate finance perspective, with a concentration on corporate bonds and security offerings. My dissertation consists of three essays. Essay One is titled “Underpricing of Corporate Bond Offerings: Evidence and Determinants”; Essay Two is titled “Calculating Abnormal Returns in Bond Market Event Studies”; and Essay Three is titled “Bond Market Reaction to Earnings Surprises and Post Earnings-Announcement Drift”. My research is partially motivated by the 2007/2008 financial crisis, which highlighted the importance of sound credit markets. In recent years, the Federal Reserve policy has pushed base interest rates to record lows, which has helped fuel trillions of corporate-bond issuance activities. “Underpricing of Corporate Bond Offerings: Evidence and Determinants” examines whether and to what extent newly-issued corporate bonds are underpriced. The availability of the TRACE database, which contains comprehensive secondary market bond transaction prices, enables me to measure bond underpricing as the return from the offering price to the secondary market price immediately after the issue. This measure of underpricing is largely adopted by equity offerings literature. Whereas other recent studies find evidence of underpricing only for speculative grade bonds and/or initial public issues, I find significant underpricing for corporate bonds across all rating classes including investment grade bonds and seasoned offerings. My results raise questions concerning the efficiency of capital raising process in the bond market. “Corporate Bond Market Event Study Methods” examines issues in the construction of corporate bond event studies using bond transaction data. Procedures used in studies to date have relatively low power to detect an event impacting bond prices. We show that this low power is largely due to the substantial heteroskedasticity in bond returns and infrequent trading. Focusing on handling these obstacles, we propose tests that yield considerably higher power. The essay “Bond Market Post-Earnings-Announcement Drift” examines the bond market post-earnings announcement return patterns. While post-earnings announcement drifts (PEAD) is, as Fama put it, the granddaddy of all market anomalies in the equity market, whether PEAD exists in corporate bond market is understudied. We find evidence of bond market PEAD following especially positive earnings surprises, suggesting that negative information gets impounded into bond prices more efficiently than positive information. We further find that bond market PEAD appears to be driven by illiquidity, which we interpret as evidence that bond market efficiency is likely hampered by lack of liquidity

Considering the Impacts of Metal Depletion on the European Electricity System

The transformation of the European electricity system could generate unintended environment-related trade-offs, e.g., between greenhouse gas emissions and metal depletion. The question thus emerges, how to shape policy packages considering climate change, but without neglecting other environmental and resource-related impacts. In this context, this study analyzes the impacts of different settings of potential policy targets using a multi-criteria analysis in the frame of a coupled energy system and life cycle assessment model. The focus is on the interrelationship between climate change and metal depletion in the future European decarbonized electricity system in 2050, also taking into account total system expenditures of transforming the energy system. The study shows, firstly, that highly ambitious climate policy targets will not allow for any specific resource policy targets. Secondly, smoothing the trade-off is only possible to the extent of one of the policy targets, whereas, thirdly, the potential of recycling as a techno-economic option is limited.</p

Power Scaling for Spatial Modulation with Limited Feedback

Spatial modulation (SM) is a recently developed multiple-input multiple-output (MIMO) technique which offers a new tradeoff between spatial diversity and spectrum efficiency, by introducing the indices of transmit antennas as a means of information modulation. Due to the special structure of SM-MIMO, in the receiver, maximum likelihood (ML) detector can be combined with low complexity. For further improving the system performance with limited feedback, in this paper, a novel power scaling spatial modulation (PS-SM) scheme is proposed. The main idea is based on the introduction of scaling factor (SF) for weighting the modulated symbols on each transmit antenna of SM, so as to enlarge the minimal Euclidean distance of modulated constellations and improve the system performance. Simulation results show that the proposed PS-SM outperforms the conventional adaptive spatial modulation (ASM) with the same feedback amount and similar computational complexity

Probing the Galactic halo with RR Lyrae stars -- IV. On the Oosterhoff dichotomy of RR Lyrae stars

We use 3653 (2661 RRab, 992 RRc) RR Lyrae stars (RRLs) with 7D (3D position, 3D velocity, and metallicity) information selected from SDSS, LAMOST, and Gaia EDR3, and divide the sample into two Oosterhoff groups (Oo I and Oo II) according to their amplitude-period behaviour in the Bailey Diagram. We present a comparative study of these two groups based on chemistry, kinematics, and dynamics. We find that Oo I RRLs are relatively more metal rich, with predominately radially dominated orbits and large eccentricities, while Oo II RRLs are relatively more metal poor, and have mildly radially dominated orbits. The Oosterhoff dichotomy of the Milky Way's halo is more apparent for the inner-halo region than for the outer-halo region. Additionally, we also search for this phenomenon in the halos of the two largest satellite galaxies, the Large and Small Magellanic clouds (LMC, SMC), and compare over different bins in metallicity. We find that the Oosterhoff dichotomy is not immutable, and varies based on position in the Galaxy and from galaxy-to-galaxy. We conclude that the Oosterhoff dichotomy is the result of a combination of stellar and galactic evolution, and that it is much more complex than the dichotomy originally identified in Galactic globular clusters