• Energy Research

Abstract The activation of the still predominantly passive demand side is necessary to further guarantee a stable power system in the short term and ensure capacity adequacy in the long run. A system with a high share of generators with nearly no marginal costs requires new services that facilitate transmitting the right economic signals to the system stakeholders. To this end we refine the notion of energy services and propose a framework to systematically design quality differentiated energy services for consumers. This approach facilitates a value-based economic assessment of energy services that deviates from the marginal-cost-paradigm. We further illustrate pricing options for these new energy service products and outline infrastructural needs and additional use case-specific product properties. Moreover, we discuss how the morphological approach can be formalised using a mathematical programming formulation and introduce a complexity measure that facilitates assessing potential adoption obstacles for end consumers. Additionally, we illustrate the practical applicability of these findings by using a prototypical implementation of a decision support system. To foster differentiated energy services, we recommend a more lenient regulatory regime lowering the barriers for new market entrants.

Abstract Electric vehicles (EVs) are a new load type with considerable temporal flexibility. This work evaluates to what extent EV fleets (based on empirical driving profiles from two distinct sociodemographic groups) can cover their charging requirements by means of variable renewable generation (wind or solar-PV). For this purpose we formulate a mixed-integer optimization problem minimizing the amount of conventional generation employed. The results indicate that the usage of variable renewable generation can be more than doubled as compared to uncoordinated charging. Furthermore, we analyze how the utilization of renewable generation by EV fleets is affected through different portfolios of renewable generation sources, charging infrastructure specifications as well as a reduced optimization horizon.

Die Einfuhrung der Smart-Meter-Technologie stellt die Energiewirtschaft in Deutschland vor grose Herausforderungen. Neben hohen Investitionen in die Zahler- und Kommunikationsinfrastruktur ist auch die Neugestaltung vieler Geschaftsprozesse erforderlich. Da die neu entstehenden Kosten nur begrenzt an Endkunden ubertragbar sind, gilt es die Aufwendungen der Energiewirtschaft durch neue Dienste und verbesserte Prozesse auf Basis von Smart Metering zu kompensieren. So ist durch die Clusteranalyse der detaillierteren Verbrauchsdaten eine deutlich feinere Kundensegmentierung auf Basis des zeitlichen Verbrauchsverhaltens moglich. Im Rahmen eines Smart-Metering-Projektes bei einem regionalen Energieversorger wurde eine Clusteranalyse fur die real vorliegenden Kundenverbrauchsdaten entwickelt und in eine Business-Intelligence-Umgebung integriert. In diesem Beitrag beschreiben und evaluieren wir dieses Artefakt im Sinne der Design Science. Wir gehen dabei insbesondere auf die Ergebnisse der Clusteranalyse von Realdaten und den moglichen Einsatz zur segmentspezifischen Tarifgestaltung ein.

Abstract 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.

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.

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.

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.

Abstract The dispatch opportunities provided by storage-enhanced Concentrating Solar Power (CSP) plants have direct implications on the investment decisions as not only nameplate capacity but also the storage capacity and the solar multiple play a crucial role for the viability of the plant investment. By integrating additional technical aspects and operation strategies, this paper extends the optimization model proposed by Madaeni et al. , How Thermal Energy Storage Enhances the Economic Viability of Concentrating Solar Power . Using a mixed integer maximization approach the paper yields both the optimal layout decision and the operation of CSP plants. Subsequently, the economic value of CSP storage is analyzed via energy modeling of a Spanish plant location under the respective wholesale market prices as well as the local feed-in tariff. The analysis shows that investment incentives for CSP plants with storage need to appropriately account for the interdependency between the price incentives and the plant operating strategy. As the resulting revenue characteristics influence the optimal size of solar field and storage differing operating strategies also give rise to differing optimal plant layouts. Most noteworthy, the current Spanish support scheme offers only limited incentives for larger thermal storage capacity.