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Flow chart of the concrete implementation of the mean-variance analysis for the Italian PV-wind mix.
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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, ass...
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... now describe the implementation in E4CLIM of the mean-variance analysis applied in the next Section 4. The corresponding flow chart is given in Figure 1. Energy, climate and geographic data are used to compute optimal mixes and their properties. ...
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... "post-processing" (quoted expressions refer to blocks in Figure 1) step translates capacities into mix properties such as the PV fraction and shortage and saturation occurrence frequencies (see plots for Italy in Section 4). This step may be further developed to compute economic costs and GHG emissions associated with a mix, etc. ...
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... compare the optimal mixes discussed so far with the actual 2015 Italian mix, it is possible to provide the latter to the E4CLIM post-processing step directly (Figure 1). This mix is represented by the gray point in Figure 4a. ...
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... series of the hourly Italian zonal electricity demand and of the yearly zonal renewable capacity factors are used to train the demand and generation models. See the "demand" and "generation data" blocks at the top of Figure 1. These variables are extracted from three publicly available databases provided respectively by the market operator GME (Gestore del Mercato Elettrico: https://www.gse.it/dati-e-scenari/statistiche) and the Transmission System Operator (TSO) Terna (https://www.terna.it/en/electric-system/statistical-data-forecast/evolution-electricitymarket). ...
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... models described in Appendix A.3 are thus used to predict these energy time series from climate data. See the "climate data" block at the top of Figure 1. In this study, one particular CORDEX regional simulation is mainly used, that we refer to as the CORDEX data. ...
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... is a limited area model, non-hydrostatic, with terrain following eta-coordinate mesoscale modeling system designed to serve both operational forecasting and atmospheric research needs [89]. The WRF simulation has been performed in the framework of HyMeX [90] and MED-CORDEX [87] programs with a 20 km horizontal resolution over the domain shown in Figure A1 between 1989 and 2012 with initial and boundary conditions provided by the ERA-interim reanalysis and updated every 6 h [91]. The WRF simulation has been relaxed towards the ERA-I large scale fields (wind, temperature and humidity) with a nudging time of 6 h [92][93][94]. ...
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... describe here the wind-production, PV-production, and demand models that are fitted to the energy data and applied to the climate data to produce the energy time series taken as input to the optimization problem; see the "Wind", "PV" and "Demand prediction" blocks at the top of Figure 1. These models aim at estimating the instantaneous demand or generation for a historical period from the climate data. ...
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Citations
... The proposed approach has been applied to the Italian electricity market, which is divided into seven market regions or zones, as shown in Figure 2. The Italian electricity market comprises a network of seven distinct market regions or zones, each characterised by its unique set of attributes and operational dynamics. These zones exhibit notable variations across multiple dimensions, including demographic composition, industrial infrastructure, energy consumption patterns, geographical features, and climatic conditions [39]. For instance, certain regions may be densely populated urban centres with high industrial activity, leading to pronounced peaks in energy demand during specific hours of the day. ...
Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability.
... Electricity demand modeling often uses multilinear models to integrate various influencing factors (Bloomfield et al., 2016(Bloomfield et al., , 2020Delort Ylla et al., 2023;Tantet et al., 2019;Toktarova et al., 2019). While such multilinear models may appear more intuitive and simpler than machine-learning models, they do not necessarily imply easier implementation and may require significant manual parameter tuning. ...
The impact of climate change on power demand and power generation has become increasingly significant. Changes in temperature, relative humidity, and other climate variables affect cooling and heating demand for households and industries and, therefore, power generation. Accurately predicting power generation is crucial for energy system planning and management. It is also crucial to understand the evolution of power generation to estimate the amount of CO2 emissions released into the atmosphere, allowing stakeholders to make informed plans to reduce emissions and to adapt to the impacts of climate change. Artificial intelligence techniques have been used to investigate energy-demand-side responses to external factors at various scales in recent years. However, few have explored the impact of climate and weather variability on power demand. This study proposes a data-driven approach to model daily power demand provided by the Carbon Monitor Power project by combining climate variables and human activity indices as predictive features. Our investigation spans the years 2020 to 2022 and focuses on eight countries or groups of countries selected to represent different climates and economies, accounting for over 70 % of global power consumption. These countries include Australia, Brazil, China, the European Union (EU), India, Russia, South Africa, and the United States. We assessed various machine-learning regressors to simulate daily power demand at the national scale. For countries within the EU, we extended the analysis to one group of countries. We evaluated the models based on key evaluating metrics: coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and median absolute error (MedAE). We also used the models to identify the most influential variables that impact power demand and determine their relationship with it. Our findings provide insight into variations in important predictive features among countries, along with the role played by distinct climate variables and indicators of the level of economic activity, such as weekends and working days, vacations and holidays, and the influence of COVID-19.
... In Morocco, there is no visible impact on REC of AC use (Bouramdane et al., 2021). In Italy, the signal on REC of the use of AC is very limited (Tantet et al., 2019) but locally the evolution of cooling needs can have a significant impact on REC (De Felice et al., 2013;Scapin et al., 2016). Therefore, analyzing the socio-economic drivers, and the energy policies of these countries and drawing inspiration from them to deploy actions adapted to the local specificities of some French regions should be considered. ...
... In Morocco, there is no visible impact on REC of AC use (Bouramdane et al., 2021). In Italy, the signal on REC of the use of AC is very limited (Tantet et al., 2019) but locally the evolution of cooling needs can have a significant impact on REC (De Felice et al., 2013;Scapin et al., 2016). Therefore, analyzing the socio-economic drivers, and the energy policies of these countries and drawing inspiration from them to deploy actions adapted to the local specificities of some French regions should be considered. ...
... First, the capacities, ω k for all k, are constrained to be positive (i.e., ω k ≥ 0 ∀k, positivity bounds on capacities). Second, instead of using the total installed capacity as an upper bound in the optimization problem [504], so we add a maximum-cost constraint to allow the model to recommission PV/PV-BES, wind and CSP/CSP-TES capacities at a cost limited by the total cost of all capacities, ω obs k for all k, installed in Morocco in 2018 (Section VII). Usually, the rental cost or the cost per installed capacity of CSP and PV plants utilizing storage with several hours will be much higher than those of plants without storage or with lower amount of storage. ...
... Nabat et al. [371] evaluate the direct and indirect effect of aerosols on solar irradiance variability in the Mediterranean. Tantet et al. [504] use the mean-variance analysis to derive regional mixes based on PV and wind accounting explicitly for climate variability in Italy. ...
... This thesis is divided into three studies to give an answer to above-stated questions by discussing a set of penetration scenarios of large-scale solar and wind capacities in prospective optimal mix; considering current and future climate conditions and addressing different combinations of onshore wind without storage and variably sizable fixed latitude-tilted multi-crystalline PV and north-south tracked horizontal parabolic trough CSP; keeping recent (2013) cost data and existing (2018) generation assets. To elaborate energy mix scenarios, we use the E4CLIM tool that allows from geographical data for specific area and from climate data to compute hourly Capacity Factors (CFs) and energy demand profiles adjusted to observations and to optimize the RE electricity mix; taking into account the time-space variability of both the generation and the demand [504]. The adaptation of E4CLIM to the four Moroccan electrical zones, including the data sources and the calibration process, and the main contribution to it are presented in chapter 2. ...
Although climate change is an inherently global issue, its impacts will not be felt equally across Earth’s pressure belts and continental-scale regions. This study seeks to examine which areas are becoming warmer and experiencing drought, with a particular focus on Africa, in light of its low historical emissions but poor economic capacity for mitigation and adaptation to climate change, and Morocco, whose conditional goal, which will be achieved with foreign assistance, is rated as “almost sufficient” but is not yet in compliance with the Paris Agreement’s goal. We also explore the consistency and sources of uncertainty in Coupled Model Intercomparison Project Phase 6 (CMIP6) models and analyze what changes from CMIP5—whose projections are based on the Representative Concentration Pathways (RCPs)—to Shared Socio-Economic Pathways (SSPs)-based scenarios for CMIP6. We find that strong forcing, with no additional climate policies, is projected to raise the mean annual temperature over Morocco for the long-term period by 6.25 °C. All CMIP6 models agree that warming (resp. drought) will be greater over land masses and poles (resp. tropical and coastal regions) than over oceans and equatorial regions (resp. high latitudes, equatorial, and monsoon zones), but less so on the intensity of changes.
... The representation of the optimal solutions in the form of Pareto fronts shows that the optimal penetration-risk ratio (the ratio between the mean penetration and the standard deviation of the hourly penetration series) is linear at low levels of penetration, while the constrained experiment shows curvature at higher levels of penetration, as the limitation on the total available IC to install makes it impossible to reach higher levels of penetration without assigning capacity to locations that carry much higher levels of risk. In the unconstrained case, the penetration-risk ratio remains constant at all penetration levels (see [60][61][62][63] for examples of these Pareto-front behaviors). The unconstrained front reveals the maximum mean-risk ratio possible for a given climate resource and associated CFs, and is itself an intrinsic property of the modeled system since it does not depend on the availability of IC. ...
... The implementation of the portfolio theory, as well as the modeling of generation and demand is performed with the e4clim model [63]. This model generates CF time series which give information on the suitability of renewable generation, independent of the eventual IC, and it creates a climate-dependent series of demand to be covered. ...
... Our approach to the optimization problem, shown here, follows the process presented in [63]. Given the hourly capacity factor , , , the hourly generation at a given location and time by renewable technology is: ...
Renewable energy planning is key to achieving target levels of renewable-energy penetration and electricity demand in the European Union. Mean–variance analysis can be used to identify the optimal spatial and technological deployment of variable renewable energy (VRE) sources in terms of maximizing VRE penetration and minimizing supply risks. We investigate the extent to which optimizing capacities at the scale of climate-data grid points, instead of administrative regions (a common approach due to data availability and computation costs), helps generate more optimal renewable deployment scenarios. A finer description of climate resources, and thus the VRE capacity factors, results in a better exploitation of complementarities, partly due to the increased degrees of freedom in the optimization. A detailed analysis of the causes behind these improvements shows that better describing local conditions leads to two advantages over less granular counterparts: higher average capacity factors and generation combinations that offer lower covariances. This analysis also reveals that more granular approaches significantly reduce variability in daily and annual climate frequencies in renewable generation under the optimal scenario. These results provide evidence of the need to account for detailed climate information to accurately identify optimal renewable deployment scenarios and support stakeholders and policy makers when it comes to making sustainable commitments.
... Portfolio theory is one of these tools and diversifies investments between multiple assets (things in which one could invest) to reduce uncertainty in total future returns with minimized loss in the expected value of returns (Ando et al., 2018). Tantet et al. (2019) use mean-variance analysis in Italy to show the benefits of spatial diversification. Spatial diversification means that the variability may be partly mitigated by aggregating the production from different sources and sites at different places. ...
... In this study, we suggest the adaptation of portfolio theory to use it as a tool to reduce the uncertainty of climate predictions when the impacts are converted to energy estimates. Solar and wind resources are often negatively correlated in space and time (Hu et al., 2019;Tantet et al., 2019). Combining the energy production from wind and solar sources reduces the variability in the power supply due to the balance between meteorological variables. ...
Solar and wind assets are climate-dependent and changes in climate will result in variations in their generation and intermittency. Developers of solar and wind parks in India have observed changes in climate conditions and variability in solar irradiation and wind profiles at the seasonal and year-to-year timescales. Future climate change perturbations, including monsoon shifts, could lead to lower-than-predicted wind and solar energy production and affect the economics of solar and wind assets. Regional climate models (RCMs) are the basis of climate impact assessments and the most trusted source of information to extract knowledge about future trends in climate variables. However, RCM projections are tainted with variability and uncertainty about the future trends. For India as a case study, we use the RCMs generated by the Coordinated Regional Climate Downscaling Experiment West Asia project (CORDEX WAS) to calculate individual wind, radiation, and temperature trends at selected sites; estimate wind and solar PV energy time series; and embed them in portfolio methods to test the impact of combining wind and solar assets on the variability of the total production and the uncertainty about the predicted production. We include a comparison of CORDEX RCMs with the ERA5 reanalysis dataset and conclude that all available RCMs reasonably simulate the main annual and seasonality features of wind speed, surface solar radiation, and temperature in India. The analysis demonstrates that the uncertainty about the portfolio return can be reduced by optimizing the combination of wind and solar assets in a producer portfolio, thus mitigating the economic impact of climate change. We find that the reduction obtained with a mixed portfolio ranges from 33 to 50% compared to a wind only portfolio, and from 30 to 96% compared to a solar only portfolio.
... First, the capacities, ω k for all k, are constrained to be positive (i.e., ω k ≥ 0 ∀k, positivity bounds on capacities). Second, instead of using the total installed capacity as an upper bound in the optimization problem [504], so we add a maximum-cost constraint to allow the model to recommission PV/PV-BES, wind and CSP/CSP-TES capacities at a cost limited by the total cost of all capacities, ω obs k for all k, installed in Morocco in 2018 (Section VII). Usually, the rental cost or the cost per installed capacity of CSP and PV plants utilizing storage with several hours will be much higher than those of plants without storage or with lower amount of storage. ...
... Nabat et al. [371] evaluate the direct and indirect effect of aerosols on solar irradiance variability in the Mediterranean. Tantet et al. [504] use the mean-variance analysis to derive regional mixes based on PV and wind accounting explicitly for climate variability in Italy. ...
... This thesis is divided into three studies to give an answer to above-stated questions by discussing a set of penetration scenarios of large-scale solar and wind capacities in prospective optimal mix; considering current and future climate conditions and addressing different combinations of onshore wind without storage and variably sizable fixed latitude-tilted multi-crystalline PV and north-south tracked horizontal parabolic trough CSP; keeping recent (2013) cost data and existing (2018) generation assets. To elaborate energy mix scenarios, we use the E4CLIM tool that allows from geographical data for specific area and from climate data to compute hourly Capacity Factors (CFs) and energy demand profiles adjusted to observations and to optimize the RE electricity mix; taking into account the time-space variability of both the generation and the demand [504]. The adaptation of E4CLIM to the four Moroccan electrical zones, including the data sources and the calibration process, and the main contribution to it are presented in chapter 2. ...
It appears that at the moment, many countries tend to favor Concentrated Solar Power (CSP) combined with its low-cost Thermal Energy Storage (TES) system over Photovoltaic (PV) as it can enhance the resilience of their energy system. However, their interplay in optimal mixes has not yet been addressed deeply enough by any study and particularly to confirm this perception in future warming climate. For instance, if the judging criteria is only money, does PV stand at a leading position? but, it is not fairly to justify those two technologies merely by cost but also by the correlation of production with peak consumption. Here comes another question: PV or its counterpart CSP? as they have distinct sensitivity to temperature and clouds. Moreover, if PV is coupled with expensive Battery Energy Storage (BES), does this mean CSP-TES will be replaced by PV-BES? This thesis discusses a set of scenarios of large-scale solar integration with wind in optimal Moroccan prospective mix under different penetration levels, storage configurations and combinations of renewable (RE) technologies. We take as objective not only to maximize the RE production, but also to reduce its variability. This Mean-Variance approach is implemented in the E4CLIM model, which we have adapted to the four Moroccan electricity zones to fit the demand model and correct biases in the Capacity Factors (CFs) calculated using climate data; ignoring the grid constraints and exchanges of electricity with neighboring countries. We add a maximum-total cost constraint to the optimization problem; and propose a method to define the rental cost of each production technology taking their dependence on the hours of production into account, which is designed in the developed storage model implemented to BES and to the added CSP-TES modules. We present, for each penetration regime, some ratios that contribute to determine what region a given capacity will be assigned to; and propose some production-demand adequacy diagnostics to evaluate which technology displaces more expensive fossil fuel generators during peak, mid and base load hours; and which one increase or reduce the curtailment. The first study addresses the questions associated with wind/PV/CSP/CSP-TES integration while the second study determines the conditions under which CSP-TES can provide an advantage against PV-BES so as to be part of the mix until a more advantageous condition prevents its integration; by examining how the integration of CSP and storage would influence the benefits from time-space complementarity in the actual climate. We conclude that contrary to the integration of CSP/CSP-TES with PV, the addition of BES to PV reveals a higher sensitivity of the mixes to solar technologies not only at low penetrations due to the reduced variability but also at high penetrations due to the differences in the storage capacity and cost. Finally, the third study assesses the impact of climate change on the resources and their implications on CFs and demand by the end-21st century relative to the historical observed forcing. We find that there are some indications of a potential impact in mixes with high penetrations but which are trivial with the eventual cost reduction effect on capacity pathways projected by climate models.However, climate change is unlikely to have a discernible effect on optimal mixes with low proportions of REs, but the key message is that the future impact on each technology is considered to be highly uncertain. We discuss the sources of uncertainty and the main options for climate-resilient RE mixes.
... The full Spanish wind mix is assessed by Santos-Alamillos et al. [22] using mean-variance optimization with ten years of simulated hourly wind CFs. Recently, Tantet et al. [23] apply mean-variance analysis to the study of the optimal recommissioning of VRE capacities in Italy using time series of both load and VRE CFs estimated from climate data. Bouramdane et al. [24] and Maimó-Far et al. [25] follow a similar approach to compare two different solar technologies in Morocco depending on the weight put on variability, and to analyze the role of the predictability of the photovoltaic (PV) production in Spain, respectively. ...
... Following Tantet et al. [23], regional VRE CF time series from 2010 to 2019 (10 y) are estimated at an hourly sampling from the climate data provided by the MERRA-2 reanalysis. This is done per grid point of the climate data, with each grid point being associated to a region, as illustrated in Figure 2. The result is averaged per region and adjusted using a Ridge linear regression with cross-validation to CF data from 2014 to 2019 (6 y) provided by the French transmission system operator, RTE (https://opendata.reseaux-energies.fr/explore/dataset/fc-tc-regionauxmensuels-eolien-solaire/information/?disjunctive.region, ...
... Similarly, the thermosensitive demand model developed by Tantet et al. [23] is used to estimate national demand time series computed from the MERRA-2 temperature data and fitted against the demand data from RTE (https://opendata.reseaux-energies.fr/explore/dataset/ eco2mix-regional-cons-def/information/?disjunctive.libelle_region&disjunctive.nature, ...
The viability of Variable Renewable Energy (VRE)-investment strategies depends on the response of dispatchable producers to satisfy the net load. We lack a simple research tool with sufficient complexity to represent major phenomena associated with the response of dispatchable producers to the integration of high shares of VRE and their impact on system costs. We develop a minimization of the system cost allowing one to quantify and decompose the system value of VRE depending on an aggregate dispatchable production. Defining the variable cost of the dispatchable generation as quadratic with a coefficient depending on macroeconomic factors such as the cost of greenhouse gas emissions leads to the simplest version of the model. In the absence of curtailment, and for particular parameter values, this version is equivalent to a mean-variance problem. We apply this model to France with solar and wind capacities distributed over the administrative regions of metropolitan France. In this case, ignoring the wholesale price effect and variability has a relatively small impact on optimal investments, but leads to largely underestimating the system total cost and overestimating the system marginal cost.
... In order to find answers to the questions stated above, we use the E4CLIM model [51,67] that allows, using climate data provided by MERRA-2 reanalysis for the four Moroccan electrical zones for the year 2018, to simulate hourly wind, PV with BES, CSP without and with TES, and demand curves adjusted to observations. We address different combinations of variably sizable PV and CSP with wind. ...
... For each strategy, an optimal mix is obtained by minimizing the variance for a given desired mean penetration or vice versa ( [67], Appendix B). Therefore, the mean-variance approach does not provide a single optimal mix but several optimal choices, referred to as the optimal frontier curve (µ(ŵ), σ(ŵ)) visualized in the mean-standard deviation graph, whereŵ denotes the vector with components giving the optimal installed capacities, ω k , for a zone-technology pair k. ...
... This mean-variance bi-objective problem is implemented in the E4CLIM model that allows to compute wind, solar PV production, energy demand adjusted to observations by using geographical data for specific area and climate data, and to optimize the renewable electricity mix; taking into account the time-space variability of both the generation and the demand [67]. ...
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.
