Search

This paper advocates the use of a dynamic scheduling model to perform battery-aware optimal task scheduling for LEO satellites, and reports on its effective application in the GOMX-3 mission. GOMX-3, a 3U CubeSat designed and built by GomSpace in less than a year, is the first European Space Agency In-Orbit Demonstration Cubesat. It was deployed from the International Space Station on October 5th 2015. GOMX-3 contains many power-hungry components, including payloads for tracking ADS-B signals from airplanes, a software-defined L-Band receiver for monitoring of geostationary satellites, and an experimental high data rate X-Band transmitter, as well as an advanced attitude control system. The operation is challenging due to capacity limits of onboard batteries as well as irregular patterns of input power and relative object attitude in space. This makes manual scheduling ineffective and error-prone, and instead suggests the need for dynamic and battery-aware scheduling of tasks. This method does not risk depletion of the onboard batteries while maximizing the amount of data delivered to the ground station. To master this challenge, the SENSATION project has developed a two-step procedure to perform optimal task scheduling for LEO satellites. The first step is a cost-optimal reachability analysis (CORA) on networks of linear priced timed automata (LPTA) modeling the satellite components and their powerrelevant behaviour. This analysis yields an optimal task schedule for the satellite relative to a linear battery model, valid up to a time horizon set by uncertainty in the satellite position due to the chaotic nature of earth's outer atmosphere. In the second step, we validate the computed schedule in a noisy setting and evaluate it along a more realistic non-linear kinetic battery model (KiBaM) having a stochastic initial state of charge, extended by battery capacity limits to bound the cumulative risk of premature battery depletion. This two-step procedure ensures that the schedules are guaranteed to exploit the available battery power most effectively, with quantifiable error bounds. We have automated and tightly integrated this procedure in the daily management of GOMX-3. Our in-orbit experiments have shown an improvement over manual scheduling as well as a clear reduction of scheduling workload. With the rising demand for larger nano-satellite constellations and more efficient payload operations, there is an obvious demand for flexible and effective resource utilization and scheduling support, working with precise battery models.

Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer’s needs. Next to the usage-specific demand profiles and location-specific production profiles, local energy storage through the use of batteries is becoming increasingly important, since it allows one to balance variations in production and demand, either locally or via the grid. Moreover, local storage can also help to ensure a continuous energy supply in the presence of grid outages, at least for a while. Hybrid Petri net (HPN) models allow one to analyze the effect of different battery management strategies on the continuity of such energy systems in the case of grid outages. The current paper focuses on one of these strategies, the so-called smart strategy, that reserves a certain percentage of the battery capacity to be only used in case of grid outages. Additionally, we introduce a new strategy that makes better use of the reserved backup capacity, by reducing the demand in the presence of a grid outage through a prioritization mechanism. This new strategy, called power-save, only allows the essential (high-priority) demand to draw from the battery during power outages. We show that this new strategy outperforms previously-proposed strategies through a careful analysis of a number of scenarios and for a selection of survivability measures, such as minimum survivability per day, number of survivable hours per day, minimum survivability per year and various survivability quantiles.

System lifetime is a major design constraint for battery-powered mobile embedded systems. The increasing gap between the energy demand of portable devices and their battery capacities is further limiting durability of mobile devices. Thus, the guarantees over Quality of Service (QoS) of battery-constrained devices under strict battery capacities are of primary interest for mobile embedded systems’ manufacturers and stakeholders. This paper presents a novel approach for deriving QoS of applications modelled as synchronous dataflow (SDF) graphs. We map these applications on heterogeneous multiprocessor platforms that are partitioned into Voltage and Frequency Islands, together with multiple kinetic battery models (KiBaMs). By modelling the whole system as hybrid automata, and applying model-checking, we evaluate, (1) system lifetime; and (2) minimum required initial battery capacities to achieve the desired application performance. We demonstrate that our approach shows a significant improvement in terms of scalability, as compared to a priced timed automata based KiBaM model. This approach also allows early detection of design errors via model checking.

Battery management strategies that reserve a certain capacity for power outages are able to increase the energy independence of a smart home. However, such strategies come at a certain cost, since these storage strategies are less flexible and energy from the grid may have to be bought at a high price, even though locally produced energy is still available in the battery, but reserved for power outage periods. This paper evaluates the cost of energy independence in smart homes with local energy generation, local storage, stochastic grid outages and seasonal demand and production profiles. As case study we use a house with matching demand and production. We provide a seasonal dependent battery management strategy, that reduces the costs of surviving grid outages. Compared to a system that maximizes self-use, the costs to achieve a survivability of 99.999% are approximately 41 euro per year for 1 MWh demand.

With the increased integration of renewable energy sources the interaction between energy producers and consumers has become a bi-directional exchange. Therefore, the electrical grid must be adapted into a smart grid which effectively regulates this two-way interaction. With the aid of simulation, stakeholders can obtain information on how to properly develop and control the smart grid. In this paper, we present the development of an integrated smart grid simulation model, using the Anylogic simulation environment. Among the elements which are included in the simulation model are houses connected to a renewable energy source, and batteries as storage devices. With the use of the these elements a neighbourhood model can be constructed and simulated under multiple scenarios and configurations. The developed simulation environment provides users better insight into the effects of running different configurations in their houses as well as allow developers to study the inter-exchange of energy between elements in a smart city on multiple levels.

The use of renewable energy in houses and neighbourhoods is very much governed by national legislation and has recently led to enormous changes in the energy market and poses a serious threat to the stability of the grid at peak production times. One of the approaches towards a more balanced grid is, e.g., taken by the German government by subsidizing local storage for solar power. While the main interest of the energy operator and the government is to balance the grid, thereby ensuring its stability, the main interest of the client is twofold: the total cost for electricity should be as low as possible and the house should be as resilient as possible in the presence of power outages. Using local battery storage can help to overcome the effects of power outages. However, the resulting resilience highly depends on the battery usage strategy employed by the controller, taking into account the state of charge of the battery. We present a Hybrid Petri net model of a house (that is mainly powered by solar energy) with a local storage unit, and analyse the impact of different battery usage strategies on its resilience for different production and consumption patterns. Our analysis shows that there is a direct relationship between resilience and flexibility, since increased resilience, i.e., reserving battery capacity for backup, decreases the flexibility of the storage unit.

Thin films of Y, La or rare earth (RE) hydrides exhibit a metal insulator transition between their di- and trihydride phase. After H loading lateral diffusion samples of these materials contain an overview of all hydride phases present in the thin film phase diagram. In this paper the thin film YH x system will be investigated. The unexpected presence of a lateral oxygen profile in the YH x sample necessitates a careful interpretation of local hydrogen concentration differences. In this paper the potential of ion beam analysis will be discussed with respect to the investigation of oxidation and hydrogenation of YH x switchable mirrors. The results of the measurements will be discussed in terms of differences between bulk- and thin-film-phase diagrams of YH x . The experimental methods used are 1 H ( 4 He, 1 H) 4 He elastic recoil detection at 5 MeV and 16 O( 4 He, 4 He) 16 O resonant backscattering around 3.036 MeV.

The use of mobile devices is often limited by the lifetime of the included batteries. This lifetime naturally depends on the battery’s capacity and on the rate at which the battery is discharged. However, it also depends on the usage pattern, i.e., the workload, of the battery. When a battery is continuously discharged, a high current will cause it to provide less energy until the end of its lifetime than a lower current. This effect is termed the rate-capacity effect. On the other hand, during periods of low or no discharge current, the battery can recover to a certain extent. This effect is termed the recovery effect. In order to investigate the influence of the device workload on the battery lifetime a battery model is needed that includes the above described effects. Many different battery models have been developed for different application areas. We make a comparison of the main approaches that have been taken. Analytical models appear to be the best suited to use in combination with a device workload model, in particular, the so-called kinetic battery model. This model is combined with a continuous-time Markov chain, which models the workload of a battery powered device in a stochastic manner. For this model, we have developed algorithms to compute both the distribution and expected value of the battery lifetime and the charge delivered by the battery. These algorithms are used to make comparisons between different workloads, and can be used to analyse their impact on the system lifetime. In a system where multiple batteries can be connected, battery scheduling can be used to “spread” the workload over the individual batteries. Two approaches have been taken to find the optimal schedule for a given load. In the first approach scheduling decisions are only taken when a change in the workload occurs. The kinetic battery model is incorporated into a priced-timed automata model, and we use the model checking tool Uppaal Cora to find schedules that lead to the longest system lifetime. The second approach is an analytical one, in which scheduling decisions can be made at any point in time, that is, independently of workload changes. The analysis of the equations of the kinetic battery model provides an upper bound for the battery lifetime. This upper bound can be approached with any type of scheduler, as long as one can switch fast enough. Both the approaches show that battery scheduling can potentially provide a considerable improvement of the system lifetime. The actual improvement mainly depends on the ratio between the battery capacity and the average discharge current.

The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round-robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper, we compare these scheduling schemes with the optimal scheduling scheme produced with two different modeling approaches: an approach based on a priced-timed automaton model (implemented and evaluated in Uppaal Cora), as well as an analytical approach (partly formulated as nonlinear optimization problem) for a slightly adapted scheduling problem. We show that in some cases the results of the simple scheduling schemes (round-robin, and best-first) are close to optimal. However, the optimal schedules, computed according to both methods, also clearly show that in a variety of scenarios, the simple schedules are far from optimal.

The use of mobile devices is limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries over the load to exploit recovery properties usually extends the system lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with a priced-timed automaton battery model (implemented and evaluated in Uppaal Cora). We see that in some cases the results of the simple scheduling schemes are close to optimal. However, the optimal schedules also clearly show that there is still room for improving the battery lifetimes.

The usage of mobile devices like cell phones, navigation systems, or laptop computers, is limited by the lifetime of the included batteries. This lifetime depends naturally on the rate at which energy is consumed, however, it also depends on the usage pattern of the battery. Continuous drawing of a high current results in an excessive drop of residual capacity. However, during intervals with no or very small currents, batteries do recover to a certain extend. We model this complex behaviour with an inhomogeneous Markov reward model, following the approach of the so-called Kinetic battery Model (KiBaM). The state-dependent reward rates thereby correspond to the power consumption of the attached device and to the available charge, respectively. We develop a tailored numerical algorithm for the computation of the distribution of the consumed energy and show how different workload patterns influence the overall lifetime of a battery.

The use of mobile devices like cell phones, navigation systems or laptop computers is limited by the lifetime of the included batteries. This lifetime depends naturally on the rate at which energy is consumed; however, it also depends on the usage pattern of the battery. Continuous drawing of a high current results in an excessive drop of residual capacity. However, during intervals with no or very small currents, batteries do recover to a certain extent. The usage pattern of a device can be well modelled with stochastic workload models. However, one still needs a battery model to describe the effects of the power consumption on the state of the battery. Over the years many different types of battery models have been developed for different application areas. In this study we give a detailed analysis of two well-known analytical models, the kinetic battery model (KiBaM) and the so-called diffusion model. We show that the KiBaM is actually an approximation of the more complex diffusion model; this was not known previously. Furthermore, we tested the suitability of these models for performance evaluation purposes, and found that both models are well suited for doing battery lifetime predictions. However, one should not draw conclusions on what is the best usage pattern based on only a few workload traces.

This paper introduces an innovative energy management system architecture for Smart Grids, designed in the European 7th framework program project e-balance. The architecture is hierarchical and fractal-like, which results in better scalability and reuse of algorithms and programming code for energy management. It supports both micro-grids and distributed energy resources. The system is structured into two main parts – the communication platform and the energy management platform. The former provides a common data exchange layer, while the latter provides the local energy management logic, currently supporting energy balancing and grid resilience services. The system is being deployed in two pilots: in Portugal and in the Netherlands, where use cases on energy balancing, grid monitoring and grid reliability will be demonstrated.

The battery lifetime of mobile devices depends on the usage pattern of the battery, next to the discharge rate and the battery capacity. Therefore, it is important to include the usage pattern in battery lifetime computations. We do this by combining a stochastic workload, modeled as a continuous-time Markov model, with a well-known battery model. For this combined model, we provide new algorithms to efficiently compute the expected lifetime and the distribution and expected value of the delivered charge.