Participation of Electric Vehicle Aggregators in Wholesale Electricity Markets: Recent Works and Future Directions

Electric Vehicles are key to reducing carbon emissions while bringing a revolution to the transportation sector. With the massive increase of EVs in road networks and the growing demand for charging services, the electric power grid faces enormous system reliability and operation stability challenges. Demand and supply disparities create inconsistency in the smooth delivery of electrical power. As a potential solution, EVs and their charging infrastructure can be aggregated to prevent the unwanted effects on power systems and also facilitate ancillary services to the power grid. When not need for transportation purposes, EVs can leverage their batteries for power grid services by participating in the electricity market via mechanisms coordinated by system operators. Hence, the market participation of EV infrastructure can help alleviate the power grid stress during peak periods. However, further research is needed to demonstrate the multiple benefits to both EV owners and power grid operators. This paper briefly overviews the existing literature on market participation of EV aggregators, discuss associated challenges and needs, and propose research directions for future research

Development of the Plug-in Electric Vehicle Charging Infrastructure via Smart-Charging Algorithms

Electricity generation and the transportation sector make up a large portion of greenhouse gas emissions in the United States. Meeting ambitious reductions in greenhouse gasses requires large scale adoption of plug-in electric vehicles (PEVs) and has led to several policies and laws aimed at incentivizing PEV sales. An inadequate charging infrastructure, however, could be a major obstacle for a large-scale adoption of PEVs. Large electrical demands from PEVs could negatively affect circuitry, increase electricity costs, and exacerbate stress to local electrical components during times of high electricity usage. These issues, however, can be addressed by deploying smart-charging strategies.This work is focused on the development of smart-charging protocols for workplace battery electric vehicle (BEV) charging. Three comprehensive smart-charging protocols with different applications are proposed. Each protocol is developed with varying degrees of focus on communication requirements and privacy concerns. The BEV-based Optimization Protocol is a decentralized, non-iterative strategy that allows BEVs to individually schedule their charging schedules. The Octopus Charger-based MILP Protocol allows octopus chargers (i.e., charging stations with multiple cables) to independently schedule charging for their assigned BEVs. The Real-Time Octopus Charger-based MILP Protocol allows octopus chargers to schedule BEV charging in real time, without prior information from BEVs. By using the appropriate cost signal and assignment algorithms, the proposed protocols can manage a parking structure demand load while reducing the number of installed charging stations. Driving patterns from the National Household Travel Survey were used to perform simulations, to verify and quantify the effectiveness of each protocol. The proposed protocols resulted in improved peak load reductions for all simulated smart-charging scenarios, when compared with uncontrolled charging. By using octopus chargers, all protocols were able to reduce the number of charging stations needed at parking structures, while meeting the charging requests of all BEVs. Time-Of-Use rate plans from Southern California Edison were used to estimate monthly electricity costs for the simulated parking structures. The smart-charging protocols resulted in reduced electricity costs for most cases studied, when compared to uncontrolled charging

Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

Managing Flexible Loads in Residential Areas

Load flexibility in households is a promising option for efficient and reliable operation of future power systems. Due to the distributed nature of residential demand, coordination mechanisms have to cope with a large number of flexible units. This thesis provides a model for demand response analysis and proposes different mechanisms for coordinating flexible loads. In particular, the potential to match intermittent output of renewable generators with electricity demand is investigated

Battery storage systems as balancing option in intermittent renewable energy systems - A transdisciplinary approach under the frame of Constructive Technology Assessment

Different battery storage technologies are considered as important flexibility option in the face of increasing shares of renewables in the grid. A challenge is to support decision-making by providing a broader perspective on battery technology development, choice, and implementation. The tailored approach in the frame of Constructive Technology Assessment (CTA) in combination with system analysis allows it to explore actor visions and expectations about battery storage and to use this information to provide quantitative information about the consequences of these. Research results combine the perspectives of technology and non-technology related actors (enactors and selectors) to create new and broader knowledge to provide “better” technology. Major implications identified for battery storage are missing business models, uncertain regulations, and doubts about their techno-economic viability. A highlight is a proof that expectations about technology characteristics in orientation to sustainability criteria are settled within concentric perspectives by using the Analytic-Hierarchy-Process (AHP). Enactors focus on economic and technological criteria which reflect the concentric bias of this group. In contrast, selectors perceive environmental and social criteria as more important. The consensus among actors regarding criteria importance is not existent to moderate which indicates that more research is required here. System analysis is used to quantify actor preferences obtained through the AHP. Li-Ion-batteries (LIB), lead-acid-batteries (VRLA), high-temperature-batteries (NaNiCl and NaS), and Vanadium-redox-flowbatteries (VRFB) are evaluated through e.g. life cycle assessment and costing for four different application fields (decentralized storage, wind energy support, primary regulation and energy-time-shift (ETS-includes compressed-air-energy-storage (CAES) and pumped-hydro-storage (PHS)). Preliminary rankings indicate that most LIBs can be recommended for all application areas, wherein decentralized storage is considered to offer the highest potentials for battery storage. VRLA and NaS achieve rather low scores whereas ranking of VRFB is highly dependent on the considered use case. PHS and CAES dominate all assessed energy storage technologies in the ETS application case

Engineering User-Centric Smart Charging Systems

Die Integration erneuerbarer Energiequellen und die Sektorenkopplung erhöhen den Bedarf an Flexibilität im Elektrizitätssystem. Elektrofahrzeuge koordiniert zu Laden bietet die Chance solche Flexibilität bereitzustellen. Allerdings hängt das Flexibilitätspotential von Elektrofahrzeugen davon ab in welchem Umfang sich die Nutzer der Fahrzeuge dazu entschließen intelligentes Laden zu nutzen. Ziel dieser Dissertation ist es Lösungen für intelligente Ladesysteme zu entwickeln, welche die Nutzer zu flexiblerem Laden anreizen und diese dabei zu unterstützen. Anhand eines Literaturüberblicks und einer Expertenbefragung werden zunächst Ziele identifiziert, welche Nutzer zu einer flexiblen Ladung motivieren können. Die Ergebnisse zeigen, dass neben finanziellen Anreizen auch die Integration erneuer-barer Energien und die Vermeidung von Netzengpässen einen Anreiz für das flexible La-den darstellen können. In der Folge wird untersucht, ob das Framing der Ladesituation hinsichtlich dieser Ziele die Ladeflexibilität von Elektrofahrzeugnutzern beeinflussen kann. Hierzu wird ein Online-Experiment mit Elektrofahrzeugnutzern evaluiert. Das sich ein Teil der Nutzer bei einem Umwelt-Framing flexibler verhält, macht Feedback darüber, wie die CO2-Emissionen von der bereitgestellten Flexibilität abhängen zu einem vielversprechenden Anreiz intelligentes Laden zu nutzen. Um solches Feedback zu er-möglichen werden als Nächstes die CO2-Einsparpotenziale eines optimierten Ladens im Vergleich zu unkontrolliertem Laden untersucht. Dazu werden die marginalen Emissions-faktoren im deutschen Stromnetz mithilfe eines regressionsbasierten Ansatzes ermittelt. Um Echtzeit-Feedback in realen Systemen zu ermöglichen wird darauf aufbauend eine Prognosemethode für Emissionsfaktoren entwickelt. Die Zielerreichung intelligenten Ladens hängt hauptsächlich von der zeitlichen und energetischen Flexibilität der Elektrofahrzeuge ab. Damit Nutzer diese Ladeeinstellungen nicht bei jeder Ankunft an der Ladestation von Hand eingeben zu müssen, könnten sie durch intelligente Assistenten unterstützt werden. Hierfür werden probabilistische Prognosen für die Flexibilität einzelner Ladevorgänge basierend auf historischen Ladevorgängen und Mobilitätsmustern entwickelt. Darüber hinaus zeigt eine Fallstudie, dass probabilistische Prognosen besser als Punktprognosen dazu geeignet sind die Ladung mehrerer Elektrofahrzeuge zu koordinieren