Christoph M. Flath

Cluster Analysis of Smart Metering Data

The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a cluster analysis of smart metering data focused on the customers’ time-based consumption behavior allows for a detailed customer segmentation. In the article we present a cluster analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a cluster analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the cluster analysis and highlight options to apply them to segment-specific tariff design.

Strategic Investment Under Uncertainty: A Synthesis

Investment is a central theme in economics, finance, and operational research. Traditionally, the focus of analysis has been either on assessing the value of flexibility (investment under uncertainty) or on describing commitment effects in competitive settings (industrial organization). Research contributions addressing the intersection of investment under uncertainty and industrial organization have become numerous in recent years. In this paper, we provide an overview aimed at categorizing and relating these research streams. We highlight managerial insights concerning the nature of competitive advantage (first- versus second-mover advantage), the manner in which information is revealed, firm heterogeneity, capital increment size, and the number of competing firms.

Charging Strategies for Battery Electric Vehicles: Economic Benchmark and V2G Potential

This paper benchmarks the economic benefits of a smart charging and a vehicle-to-grid (V2G) operating strategy of an electric vehicle (EV) against a zero-intelligence charging strategy in a simulation-based analysis. Smart charging optimizes the timing of EV charging, while V2G additionally allows for selling electricity to the energy market. The strategies internalize the wear on energy storage equipment. The simulation builds on over 11 400 empirical driving profiles from a German mobility panel and applies technical specifications of three currently available EVs. This allows for a realistic analysis of the applicability of smart charging and V2G as well as the extent to which EVs are capable to fulfill current driving needs. Results show that smart charging strategies will reduce charging cost on average by more than 32% in all analyzed cases. V2G could provide additional revenue for EV owners with shorter trip patterns while reducing electricity cost for others.

Improving Electric Vehicle Charging Coordination Through Area Pricing

Meeting charging demands of large electric vehicle fleets will raise electrical load significantly and may pose challenges for todays 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.

Energy Informatics - Current and Future Research Directions

Due to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The article attempts to characterize this young and dynamic field of research by describing current EI research topics and methods and provides an outlook of how the field might evolve in the future. It is shown that two general research questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage information and communication technology (ICT) to (1) improve energy efficiency, and (2) to integrate decentralized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the respective research questions are broken down into specific research projects and how EI researchers have made contributions based on their individual academic background.

The Economics of Multi-Hop Ride Sharing

Ride sharing allows to share costs of traveling by car, e.g., for fuel or highway tolls. Furthermore, it reduces congestion and emissions by making better use of vehicle capacities. Ride sharing is hence beneficial for drivers, riders, as well as society. While the concept has existed for decades, ubiquity of digital and mobile technology and user habituation to peer-to-peer services and electronic markets have resulted in particular growth in recent years. This paper explores the novel idea of multi-hop ride sharing and illustrates how information systems can leverage its potential. Based on empirical ride sharing data, we provide a quantitative analysis of the structure and the economics of electronic ride sharing markets. We explore the potential and competitiveness of multi-hop ride sharing and analyze its implications for platform operators. We find that multi-hop ride sharing proves competitive against other modes of transportation and has the potential to greatly increase ride availability and city connectedness, especially under high reliability requirements. To fully realize this potential, platform operators should implement multi-hop search, assume active control of pricing and booking processes, improve coordination of transfers, enhance data services, and try to expand their market share.

A Revenue Management Approach for Efficient Electric Vehicle Charging Coordination

Ambitious goals of electric vehicle (EV) penetration may conflict with the capabilities of todays power system. Especially simultaneous charging at home may lead to significant load spikes or grid stability issues. Prior research has identified the need for appropriate coordination approaches. This research focuses mostly on coordinating system loads ignoring local grid constraints. The suggested mechanisms either are centralized control approaches ignoring user preferences or based on the electrical energy cost. We propose to complement these approaches by using mechanisms from revenue management for perishable assets. First, we formalize charging coordination as a minimal revenue management problem and then derive an appropriate advance sale mechanism. By accounting for heterogeneous customer segments, this approach can achieve a socially efficient allocation of available charging capacity. Using a local neighborhood scenario, we evaluate the impact of such an approach.

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

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.

Deadline differentiated pricing in practice: marketing EV charging in car parks

Electric vehicle charging is considered as a prime case of load flexibility in future smart grids. We examine a scenario where electric vehicles are charged in a car park with local photovoltaic (PV) generation. In this setting, temporal charging flexibility can be leveraged to increase utilization of local generation. To incentivize flexible loads we apply a deadline differentiated pricing scheme. Prices are set by the car park operator in a profit-maximizing manner in settings with varying PV capacity and costs of conventional generation. This allows us to assess the value of flexibility passed on to customers in form of discounts. Furthermore, we determine the minimum flexibility level qualifying for this discount. By and large, absolute price levels and flexibility discounts are mainly driven by the cost of conventional generation. On the other hand, the minimum flexibility requirements are affected by both the costs of conventional generation as well as the local PV capacity: higher costs of conventional generation as well as larger PV capacities will decrease this threshold.

Local matching of flexible load in smart grids

Today’s power systems are experiencing a transition from primarily fossil fuel based generation toward greater shares of renewable energy sources. It becomes increasingly costly to manage the resulting uncertainty and variability in power system operations solely through flexible generation assets. Incorporating demand side flexibility through appropriately designed incentive structures can add an additional lever to balance demand and supply. Based on a supply model using empirical wind generation data and a discrete model of flexible demand with temporal constraints, we design and evaluate a local online market mechanism for matching flexible load and uncertain supply. Under this mechanism, truthful reporting of flexibility is a dominant strategy for consumers reducing payments and increasing the likelihood of allocation. Suppliers, during periods of scarce supply, benefit from elevated critical-value payments as a result of flexibility-induced competition on the demand side. We find that, for a wide range of the key parameters (supply capacity, flexibility level), the cost of ensuring incentive compatibility in a smart grid market, relative to the welfare-optimal matching, is relatively small. This suggests that local matching of demand and supply can be organized in a decentral manner in the presence of a sufficiently flexible demand side. Extending the stylized demand model to include complementary demand structures, we demonstrate that decentral matching induces only minor efficiency losses if demand is sufficiently flexible. Furthermore, by accounting for physical grid limitations we show that flexibility and grid capacity exhibit complementary characteristics.