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In this article, we investigate mismatch of renewable electricity production to demand and how this is affected by flexibility options on the supply side. We assess the impact of spatial and temporal smoothing on reliability of production and whether they can reduce risks of variation. As a case study we pick a simplified (partial) representation of the Norwegian electricity system and focus on wind power. We represent regional electricity production and demand through two stochastic processes: the wind capacity factors are modelled as a two-dimensional Ornstein-Uhlenbeck process and electricity demand consists of realistic base load and temperature-induced load coming from a deseasonalised autoregressive process. We validate these processes, that we have trained on historical data, through Monte Carlo simulations allowing us to generate many statistically representative weather years. For the investigated realisations (weather years) we study deviations of production from demand under different wind capacities, and introduce different scenarios where flexibility options like storage and transmission is available. Our analysis shows that simulated loss values are reduced significantly by cooperation and any mode of flexibility. Combining storage and transmission leads to even more synergies and helps to stabilise production levels and thus adequacy of renewable power systems.

<p>To fulfil the Paris agreement, European countries have set out decarbonisation targets for 2050 and to reach them plan to massively expand the deployment of renewable energy technologies. Within the European Union, the member states are responsible for mapping out national strategies that meet the overarching EU objectives, including those of the recent REPowerEU plan. At the same time, the European electricity network is highly integrated with many interdependencies such that national policy decisions already have cross borders effects.</p> <p><br />We study trade-offs between design flexibility on a regional level and in the entire network, and whether decisions by some actors (i.e. countries, regions or the EU) can enable or restrict the choices of others. This is done using the open sector-coupled energy system optimisation model PyPSA-Eur-Sec at a high spatial and temporal resolution, aiming at carbon-neutral scenarios for 2050. We define design flexibility in the context of near-optimal feasible spaces ---  using recent advances we are able to approximate the joint near-optimal feasible space for both a particular region and the rest of the system. By intersecting near-optimal spaces for different scenarios, we make this approach robust to uncertainties including weather variability and technology costs. For a number of selected regions in Europe, we thus look for both regional and European investment decisions which enable or restrict agency by enlarging or shrinking the space of solutions compatible with the decarbonisation targets for 2050.</p>

<p>The climate crisis and cost reductions in key technologies like solar, wind, and batteries are pushing an ambitious transition of power systems to renewables. This shift towards intermittent generation deepens the impact of weather and climate on the energy sector and can introduce new risks if not accounted for properly. Furthermore, as the ramifications of climate change are only to become even more noticeable, extreme events are expected to increase both in frequency and intensity. This in itself may lead to additional stress on renewable power systems, and recent research in energy meteorology relates these extreme weather events to so-called compound weather events, which are caused by more than one variable or component at the same time. However, compound events, their characteristics, and their risk for energy systems design are not yet well understood.</p> <p>In this work, we use outputs of a power system model to identify what meteorological drivers lead to difficult periods and stress in the European electricity system. For this we couple energy modelling and meteorology in an iterative process that can connect weather insights from a synoptic scale to features of a highly resolved representation of the European electricity network. We use the open energy system model PyPSA-Eur with four decades of reanalysis weather data to find cost-minimal solutions for a fully decarbonised European power system. Dual variables of these optima are used to identify difficult weather periods, understood as periods that drive system design and total cost. This use of dual variables of the optima - as opposed to studying weather data in isolation - allows for a more accurate identification of difficult periods, tailored to the energy system at hand. We then characterise the underlying weather conditions during those periods and assess their effects on the power system and energy variables. Due to the level of integration, some of these spread across the entire continent, whereas other phenomena remain local; they can be of varied intensity and persist on different time scales.</p> <p>Bringing an enhanced understanding of which weather events are difficult for energy systems, this approach can help to find obstacles for a transition to a fully renewable European power network, and inform how certain risks can be avoided or resilience strengthened.</p>