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Modern production systems are characterized by the increasingly use of CPS and IoT networks. However, processing the available information for adaptation and reconfiguration often occurs in relatively large time cycles. It thus does not take advantage of the optimization potential available in the short term. In this paper, a concept is presented that, considering the process information of the individual heterogeneous system elements, detects optimization potentials and performs or proposes adaptation or reconfiguration. The concept is evaluated utilizing a case study in a learning factory. The resulting system thus enables better exploitation of the potentials of the CPPS.
Modern power DMOS transistors greatly benefit from the continuous advances of the technology, which yield devices with very low area-specific RDS,on figures of merit and therefore allow for significantly reduced active areas. However, in many applications, where the devices must dissipate high amounts of energy and thus are subjected to significant self-heating, the active area is not dictated by RDS,on requirements, but by the energy constraints. In this paper, a simple method of improving the energy capability and reliability of power DMOS transistors operating in pulsed conditions is proposed and experimentally verified. The method consists in redistributing the power density from the hotter to the cooler device regions, hence achieving a more homogeneous temperature distribution and a reduced peak temperature. To demonstrate the principle, a simple gate offset circuit is used to redistribute the current density to the cooler DMOS parts. No technology changes are needed for the implementation, only minor changes to the driver circuit are necessary, with a minimal impact on the additional required active area. Improvements in the energy capability from 9.2% up to 39% have been measured. Furthermore, measurements have shown that the method remains effective also if the operating conditions change significantly. The simplicity and the effectiveness of the implementation makes the proposed method suitable to be used in a wide range of applications.
This paper covers test and verification of a forecast-based Monte Carlo algorithm for an optimized, demand-oriented operation of combined heat and power (CHP) units using the hardware-in-the-loop approach. For this purpose, the optimization algorithm was implemented at a test bench at Reutlingen University for controlling a CHP unit in combination with a thermal energy storage, both in real hardware. In detail, the hardware-in-the-loop tests are intended to reveal the effects of demand forecasting accuracy, the impact of thermal energy storage capacity and the influence of load profiles on demand-oriented operation of CHP units. In addition, the paper focuses on the evaluation of the content of energy in the thermal energy storage under practical conditions. It is shown that a 5-layer model allows to determine the energy stored quite accurately, which is verified by experimental results. The hardware-in-the-loop tests disclose that demand forecasting accuracies, especially electricity demand forecasting, as well as load profiles strongly impact the potential for CHP electricity utilization on-site in demand-oriented mode. Moreover, it is shown that a larger effective capacity of the thermal energy storage positively affects demand-oriented operation. In the hardware-in-the-loop tests, the fraction of electricity generated by the CHP unit utilized on-site could thus be increased by a maximum of 27% compared to heat-led operation, which is still the most common modus operandi of small-scale CHP plants. Hence, the hardware-in-the-loop tests were adequate to prove the significant impact of the proposed algorithm for optimization of demand-oriented operation of CHP units.
Based on a survey among customers of seven German municipal utilities, we estimate two regression models to identify the most prospective customer segments and their preferences and motivations for participating in peer-to-peer (P2P) electricity trading and develop implications for decision-makers in the energy sector and policy-makers for this currently relatively unknown product. Our results show a large general openness of private households towards P2P electricity trading, which is also the main predictor of respondents' intention to participate. It is mainly influenced by individuals’ environmental attitude, technical interest, and independence aspiration. Respondents with the highest willingness to participate in P2P electricity trading are mainly motivated by the ability to share electricity, and to a lesser extent by economic reasons. They also have stronger preferences for innovative pricing schemes (service bundles, time-of-use tariffs). Differences between individuals can be observed depending on their current ownership (prosumers) or installation probability of a microgeneration unit (consumers, planners). Rather than current prosumers, especially planners willing to install microgeneration in the foreseeable future are considered to be the most promising target group for P2P electricity trading. Finally, our results indicate that P2P electricity trading could be a promising niche option in the German energy transition.
Willingness-to-pay for alternative fuel vehicle characteristics : a stated choice study for Germany
(2016)
In the light of European energy efficiency and clean air regulations, as well as an ambitious electric mobility goal of the German government, we examine consumer preferences for alternative fuel vehicles (AFVs) based on a Germany-wide discrete choice experiment among 711 potential car buyers. We estimate consumers’ willingness to-pay and compensating variation (CV) for improvements in vehicle attributes, also taking taste differences in the population into account by applying a latent class model with 6 distinct consumer segments. Our results indicate that about 1/3 of the consumers are oriented towards at least one AFV option, with almost half of them being AFV-affine, showing a high probability of choosing AFVs despite their current shortcomings. Our results suggest that German car buyers’ willingness-to-pay for improvements of the various vehicle attributes varies considerably across consumer groups and that the vehicle features have to meet some minimum requirements for considering AFVs. The CV values show that decision-makers in the administration and industry should focus on the most promising consumer group of ‘AFV aficionados’ and their needs. It also shows that some vehicle attribute improvements could increase the demand for AFVs cost-effectively, and that consumers would accept surcharges for some vehicle attributes at a level which could enable their private provision and economic operation (e.g. fast-charging infrastructure). Improvement of other attributes will need governmental subsidies to compensate for insufficient consumer valuation (e.g. battery capacity).
Due to the lack of sophisticated component libraries for microelectromechanical systems (MEMS), highly optimized MEMS sensors are currently designed using a polygon driven design flow. The advantage of this design flow is its accurate mechanical simulation, but it lacks a method for analyzing the dynamic parasitic electrostatic effects arising from the electric coupling between (stationary) wiring and structures in motion. In order to close this gap, we present a method that enables the parasitics arising from in-plane, sensor-structure motion to be extracted quasi-dynamically. With the method's structural-recognition feature we can analyze and optimize dynamic parasitic electrostatic effects.
Wave-like differential equations occur in many engineering applications. Here the engineering setup is embedded into the framework of functional analysis of modern mathematical physics. After an overview, the –Hilbert space approach to free Euler–Bernoulli bending vibrations of a beam in one spatial dimension is investigated. We analyze in detail the corresponding positive, selfadjoint differential operators of 4-th order associated to the boundary conditions in statics. A comparison with free string wave swinging is outlined.
In an effort to make the cultural and institutional aspects of energy efficiency in industrial organizations more visible, this article introduces a theoretical framework of decision-making processes. Taking a sociological perspective and viewing organizations as cultural systems embedded in wider social contexts, I have developed a multilevel framework addressing institutional, organizational, and individual dimensions shaping decisions on energy efficiency. The framework's development is based on qualitative empirical fieldwork and integrates insights into organizational theory; neo-institutional theory, the attention-based view of the firm, and organizational culture theories. I conclude that decisions on energy efficiency are results of problematization and theorization processes. These processes emerge between the institutional issue-field, the organization, and its members. The model explains decisions shaped by environment (external and material), organizational processes (energy-efficiency practices, climate and culture) and individuals’ characteristics. The framework serves several purposes: introducing a meta-theory of decision making, providing a concept for empirical analysis, and enabling connectivity to the research on barriers.
Induced by a societal decision to phase out conventional energy production - the so-called Energiewende (energy transition) - the rise of distributed generation acts as a game changer within the German energy market. The share of electricity produced from renewable resources increased to 31,6% in 2015 (UBA, 2016) with a targeted share of renewable resources in the electricity mix of 55%-60% in 2035 (RAP, 2015), opening perspectives for new products and services. Moreover, the rapidly increasing degree of digitization enables innovative and disruptive business models in niches at the grid's edge that might be the winners of the future. It also stimulates the market entry of newcomers and competitors from other sectors, such as IT or telecommunication, challenging the incumbent utilities. For example, virtual and decentral market places for energy are emerging; a trend that is likely to speed up considerably by blockchain technology, if the regulatory environment is adjusted accordingly. Consequently, the energy business is turned upside down, with customers now being at the wheel. For instance, more than one-third of the renewable production capacities are owned by private persons (Trendsearch, 2013). Therefore, the objective of this chapter is to examine private energy consumer and prosumer segments and their needs to derive business models for the various decentralized energy technologies and services. Subsequently, success factors for dealing with the changing market environment and consequences of the potentially disruptive developments for the market structure are evaluated.
Business opportunities for energy providers to utilize flexible industrial demand are platform-based, connecting small and medium-sized enterprises (SMEs) to a virtual power plant (VPP) in complex ecosystems. Unlike in other VPPs, the focus is on participation, data, and control sovereignty for the SMEs. An exemplary application for an existing cement mill demonstrates positive margins. Viable VPP business models for small and medium-sized utilities include the “orchestrator,” i.e., adding value by linking services of specialized providers, the “integrator,” i.e., incorporating internal and external processes and resources, as well as the “white label user,” i.e., using a turn-key VPP from an exclusive cooperation partner.