Interest in power-to-gas (P2G) as an energy storage technology is increasing, since it allows to utilise the existing natural gas infrastructure as storage medium, which reduces capital investments and facilitates its deployment. P2G systems using renewable electricity can also substitute for fossil fuels used for heating and transport. In this study, both techno-economic and life cycle assessment (LCA) are applied to determine key performance indicators for P2G systems generating hydrogen or methane (synthetic natural gas -SNG) as main products. The proposed scenarios assume that P2G systems participate in the Swiss wholesale electricity market and include several value-adding services in addition to the generation of low fossil-carbon gas.We find that none of the systems can compete economically with conventional gas production systems when only selling hydrogen and SNG. For P2G systems producing hydrogen, four other services such as heat and oxygen supply are needed to ensure the economic viability of a 1 MW P2H system. CO 2 captured from the air adds $50/MWh t of extra levelised cost to SNG compared to CO 2 supplied from biogas upgrading plants and it does not offer an economic case yet regardless of the number of services. As for environmental performance, only the input of "clean" renewable electricity to electrolysis result in environmental benefits for P2G compared to conventional gas production. In particular, more than 90% of the life cycle environmental burdens are dominated by the electricity supply to electrolysis for hydrogen production, and the source of CO 2 in case of SNG.

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16Given the increasing penetration of renewable energy technologies as distributed generation 17 embedded in the consumption centres, there is growing interest in energy storage systems 18 located very close to consumers. These systems allow to increase the amount of renewable 19 energy generation consumed locally, they provide opportunities for demand-side 20 management and help to decarbonise the electricity, heating and transport sectors. 21In this paper, the authors present an interdisciplinary review of community energy storage 22 (CES) with a focus on its potential role and challenges as a key element within the wider 23 energy system. The discussion includes: the whole spectrum of applications and 24 technologies with a strong emphasis on end user applications; techno-economic, 25 environmental and social assessments of CES; and an outlook on CES from the customer, 26 utility company and policy-maker perspectives. Currently, in general only traditional thermal 27 storage with water tanks is economically viable. However, CES is expected to offer new 28 opportunities for the energy transition since the community scale introduces several 29 advantages for electrochemical technologies such as batteries. Technical and economic 30 benefits over energy storage in single dwellings are driven by enhanced performance due to 31 less spiky community demand profile and economies of scale respectively. In addition, CES 32 brings new opportunities for citizen participation within communities and helps to increase 33 awareness of energy consumption and environmental impacts. 34

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We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains.

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Energy storage can help integrate local renewable generation, however the best deployment level for storage remains an open question. Using a data-driven approach, this paper simulates 15-minute electricity consumption for households and groups them into local communities of neighbors using real locations and the road network in Cambridge, MA. We then simulate PV for these households and use this framework to study battery economics in a high PV adoption, high electricity cost scenario, in order to demonstrate significant storage adoption. We compare the results of storage adoption at the level of individual households to storage adoption on the community level using the aggregated community demands. Under the simulated conditions, we find that the optimum storage at the community level was 65% of that at the level of individual households and each kWh of community battery installed was 64-94% more effective at reducing exports from the community to the wider network. Therefore, given the current increasing rates of residential battery deployment, our research highlights the need for energy policy to develop market mechanisms which facilitate the deployment of community storage.