Sebastian Gottwalt

Bio: Sebastian Gottwalt is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Demand response & Electric power system. The author has an hindex of 8, co-authored 15 publications receiving 789 citations. Previous affiliations of Sebastian Gottwalt include Forschungszentrum Informatik.

Modeling and Valuation of Residential Demand Flexibility for Renewable Energy Integration

Abstract: The share of renewable generation (RG) in the energy mix has seen constant growth in recent years. RG is volatile and not (fully) controllable. Consequently, the alignment of stochastic demand with supply, which is fundamental for ensuring grid stability, becomes more difficult. The utilization of demand side flexibility as well as RG portfolio design are attractive opportunities to avoid excessive investments in conventional power plants and costs for balancing power. This paper provides a comprehensive centralized scheduling model to exploit demand flexibility from residential devices. We analyze the monetary value of households for demand response (DR) by determining the potential of various current and possible available future end consumer devices to reduce generation costs of a flexibility aggregator in a microgrid with a large share of RG. Furthermore, we identify key characteristics affecting the value of demand flexibility and derive recommendations for an aggregator’s RG portfolio structure. Our simulation results indicate that electric vehicles, stationary batteries, and storage heaters are the most promising devices for residential DR. Furthermore, we show that the potential of a device to directly utilize intermittent RG is largely influenced by the composition of the renewable energy source portfolio.

Improving Electric Vehicle Charging Coordination Through Area Pricing

Abstract: Meeting charging demands of large electric vehicle fleets will raise electrical load significantly and may pose challenges for today's power system. Appropriate coordination of electric vehicle charging can reduce these threats. Acknowledging the interdependency between the transportation and the power system created by electric vehicles, we develop a charging coordination model based on German mobility data. We extend the prior work by explicitly accounting for both the temporal and the spatial dimension. We are thus able to analyze the loads from price-based EV fleet charging while at the same time accounting for distribution grid constraints. Furthermore, we propose a heuristic charging strategy based on limited trip and price information. Our results show that the sole use of time-based electricity prices for the coordination of electric vehicle charging produces high load spikes independent of the charging strategies and power levels. These peaks are induced by simultaneous charging activity and may cause stability problems within distribution grids in residential areas. To mitigate these load spikes, we introduce a spatial price component that reflects local capacity utilization. These local prices induce both a temporal and spatial shift of charging activity that mitigates the load spikes.

Assessing load flexibility in smart grids: Electric vehicles for renewable energy integration

Abstract: Demand response can contribute to system stability and foster integration of renewable energy sources. In our work we model static residential electricity demand together with flexible electric vehicles (EVs) as charging loads. We develop a mixed-integer program to assess the ability of an EV fleet operator to coordinate charging in such a way that a maximum amount of renewable energy is used. Such coordinated charging still requires that all projected mobility needs are satisfied. EVs are modeled using empirical driving profiles of full time employees. Our results show that compared to uncoordinated immediate charging, an optimized charging schedule can nearly double the share of renewable energy used and achieve a yearly supply from wind power of up to 67.2%. In addition, we find that coordinated charging decreases load peaks and reduces the amount of conventional generation required as backup capacity.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Abstract: The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

Energy storage in the energy transition context: A technology review

Abstract: Concerns about climate change as well as fossil fuel usage restrictions motivate the energy transition to a sustainable energy sector requiring very high penetration level of renewable energy sources in the World energy matrix, including those heavily hydrocarbon-based as fuel for transportation. Some of these renewable sources have an uncontrollable output and managing the variability is challenging. The current upward trend in renewables participation will demand even more flexibility from the energy systems. Among several options for increasing flexibility, energy storage (ES) is a promising one considering the variability of many renewable sources. The purpose of this study is to present a comprehensive updated review of ES technologies, briefly address their applications and discuss the barriers to ES deployment. Methodology involves the description and the analysis of ES many existing and developing technologies. ES applications are discussed briefly using logistic and parametric classification logics. As result of this study, it will be pointed out that no ES technology outstands simultaneously in all technical characteristics and consequently, selection should be driven on a case base analysis. Economic feasibility of ES business models and establishment of a well-suited regulatory environment are major issues to unlock ES deployment. Regarding energy transition, Power-to-Gas, Power-to-Liquids and Solar-to-Fuel technologies are very promising and further studies about these technologies are required to better understand their possibilities and how to overcome the barriers to their practical usage.

Home energy management systems: A review of modelling and complexity

Abstract: Innovations in the residential sector are required to reduce environmental impacts, as the sector is a contributor to greenhouse gas emissions. The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and productions schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles and consumer comfort. The existing literature has presented several methods, such as mathematical optimization, model predictive control and heuristic control, for creating efficient operation schedules and for making good consumption and production decisions. However, the effectiveness of the methods in the existing literature can be difficult to compare due to diversity in modelling parameters, such as appliance models, timing parameters and objectives. The present chapter provides a comparative analysis of the literature on HEMS, with a focus on modelling approaches and their impact on HEMS operations and outcomes. In particular, we discuss a set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations and modelling consumer well-being. The presented work is organized to allow a reader to understand and compare the important considerations, approaches, nomenclature and results in prominent and new literary works without delving deeply into each one.

Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects

Abstract: Electric vehicles can become integral parts of a smart grid, since they are capable of providing valuable services to power systems other than just consuming power. On the transmission system level, electric vehicles are regarded as an important means of balancing the intermittent renewable energy resources such as wind power. This is because electric vehicles can be used to absorb the energy during the period of high electricity penetration and feed the electricity back into the grid when the demand is high or in situations of insufficient electricity generation. However, on the distribution system level, the extra loads created by the increasing number of electric vehicles may have adverse impacts on grid. These factors bring new challenges to the power system operators. To coordinate the interests and solve the conflicts, electric vehicle fleet operators are proposed both by academics and industries. This paper presents a review and classification of methods for smart charging (including power to vehicle and vehicle-to-grid) of electric vehicles for fleet operators. The study firstly presents service relationships between fleet operators and other four actors in smart grids; then, modeling of battery dynamics and driving patterns of electric vehicles, charging and communications standards are introduced; after that, three control strategies and their commonly used algorithms are described; finally, conclusion and recommendations are made.