• Energy Research

The trajectory outlined in the Paris Agreement to keep global warming below 2°C dictates not only the timing but also the speed at which the transformation of our energy system must take place to decarbonize energy production. Complying with the Paris Agreement requires reducing the carbon content of energy by about 75% and therefore making a rapid transition from fossil production to production based on low-carbon technologies. Among these technologies are those based on renewable energies. The variability of the climate itself induces a fluctuating or even an intermittent production of variable renewable energy (solar, wind, marine), challenging the balance of the electricity grid. In this context, to speak of energy transition is to face the problem of increasing the penetration of low-carbon energy production while limiting the variability while ensuring socio-technical feasibility and economic viability. The problem is not simple and the delicate balance between urgency (drastic reduction in emissions) and utopia (what strategy for low carbon energies, opportunities and obstacles) needs to be clearly defined.

In this study, we examine how Battery Storage (BES) and Thermal Storage (TES) combined with solar Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies with an increased storage duration and rental cost together with diversification would influence the Moroccan mix and to what extent the variability (i.e., adequacy risk) can be reduced; this is done using recent (2013) cost data and under various penetration scenarios. To do this, we use MERRA-2 climate reanalysis to simulate hourly demand and capacity factors (CFs) of wind, solar PV and CSP without and with increasing storage capabilities—as defined by the CSP Solar Multiple (SM) and PV Inverter Loading Ratio (ILR). We adjust these time series to observations for the four Moroccan electrical zones over the year 2018. Our objective is to maximize the renewable (RE) penetration and minimize the imbalances between RE production and consumption considering three optimization strategies. We analyze mixes along Pareto fronts using the Mean-Variance Portfolio approach—implemented in the E4CLIM model—in which we add a maximum-cost constraint to take into account the different rental costs of wind, PV and CSP. We propose a method to calculate the rental cost of storage and production technologies taking into account the constraints on storage associated with the increase of SM and ILR in the added PV-BES and CSP-TES modules, keeping the mean solar CFs fixed. We perform some load bands-reduction diagnostics to assess the reliability benefits provided by each RE technology. We find that, at low penetrations, the maximum-cost budget is not reached because a small capacity is needed. The higher the ILR for PV, the larger the share of PV in the mix compared to wind and CSP without storage is removed completely. Between PV-BES and CSP-TES, the latter is preferred as it has larger storage capacity and thus stronger impact in reducing the adequacy risk. As additional BES are installed, more than TES, PV-BES is favored. At high penetrations, optimal mixes are impacted by cost, the more so as CSP (resp., PV) with high SM (resp., ILR) are installed. Wind is preferably installed due to its high mean CF compared to cost, followed by either PV-BES or CSP/CSP-TES. Scenarios without or with medium storage capacity favor CSP/CSP-TES, while high storage duration scenarios are dominated by low-cost PV-BES. However, scenarios ignoring the storage cost and constraints provide more weight to PV-BES whatever the penetration level. We also show that significant reduction of RE variability can only be achieved through geographical diversification. Technological complementarity may only help to reduce the variance when PV and CSP are both installed without or with a small amount of storage. However, the diversification effect is slightly smaller when the SM and ILR are increased and the covariances are reduced as well since mixes become less diversified.

International audience

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e4clim’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results.

We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction.