The emergence of energy-scavenging techniques for powering networks of embedded devices is raising the need for dedi-cated simulation frameworks that can support researchers and developers in the design and performance evaluation of harvesting-aware protocols and algorithms. In this work we present GreenCastalia, an open-source energy-harvest-ing simulation framework we have developed for the popu-lar Castalia simulator. GreenCastalia supports multi-source and multi-storage energy harvesting architectures, it is highly modular and easily customizable. In addition, it allows to simulate networks of embedded devices with heterogeneous harvesting capabilities.

Self-sustainability is a crucial step formodern sensor networks. Here, we offer an original and comprehensive framework for autonomous sensor networks powered by renewable energy sources. We decompose our design into two nested optimization steps: the inner step characterizes the optimal network operating point subject to an average energy consumption constraint, while the outer step provides online energy management policies that make the system energetically self-sufficient in the presence of unpredictable and intermittent energy sources. Our framework sheds new light into the design of pragmatic schemes for the control of energy-harvesting sensor networks and permits to gauge the impact of key sensor network parameters, such as the battery capacity, the harvester size, the information transmission rate, and the radio duty cycle. We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state-of-the-art solutions, and describing the impact of relevant design choices in terms of achievable network throughput and battery-level dynamics.

Abstract—Energy autonomy and system lifetime are critical concerns in wireless sensor networks (WSNs), for which energy harvesting (EH) is emerging as a promising solution. Neverthe-less, the tools supporting the design of EH-WSNs are limited to a few simulators that require developers to re-implement the application with programming languages different from WSN ones. Further, simulators notoriously provide only a rough ap-proximation of the reality of low-power wireless communication. In this paper we present SENSEH, a software framework that allows developers to move back and forth between the power and speed of a simulated approach and the reality and accuracy of in-field experiments. SENSEH relies on COOJA for emulating the actual, deployment-ready code, and provides two modes of operation that allow the reuse of exactly the same code in real-world WSN deployments. We describe the toolchain and software architecture of SENSEH, and demonstrate its practical use and benefits in the context of a case study where we investigate how the lifetime of a WSN used for adaptive lighting in road tunnels can be extended using harvesters based on photovoltaic panels. I.

Abstract—The push to incorporate more renewable sources of generation into electricity grids is forcing a sea change in how grids are composed and managed. The operational task of balancing an electricity grid has grown from a relatively simple, solvable problem to one of great complexity, involving widespread prediction and coordination backed with substantial computational resources. New types of electric resources, particu-larly on the demand-side, are emerging in order to accommodate the fluctuations from this groundswell of renewable generation. In addition to the massive amount of capacity being added to utility electricity generation fleets, large amounts of distributed generation are being added on the customer side of electricity meters with unclear effects on grids as a whole. In this work, we assess the impact on the requirements for demand-side electricity resources from growth in both utility-scale and distributed renewable generation fleets. To do this, we construct models of the California electricity grid, both now and in a future scenario with 50 % renewables penetration, as well as a model that incorporates data on over 125,000 rooftop photovoltaic installations. We then examine the context of these scenarios to understand the role and range of demand-side resources on these future, more renewable electricity grids. From this analysis, we show that a future grid for the state of California with 50 % renewable generation and 5 times the distributed generation capacity of the current grid would reduce the overall fraction of total electricity delivered from thermal (natural gas) generation by over two-thirds, but would increase the frequency of large (±2GW) hourly ramps in thermal generation by a factor larger than five. Combating this enormous increase in ramps is a prime opportunity for demand-side management to increase the proportion of renewables as well as reduce the cost of electricity in future electric grids. I.