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• Mechanisms to encourage clean energy in Spain from 2005 to the present are analyzed. • FiT and FiP mechanisms can be effective in promoting new renewable projects. • Experience suggests that excessive support can jeopardize the entire market. • A transitional retribution scheme has proven to be effective if correctly supervised. This research paper delves into the central mechanisms implemented in Spain to foster the adoption of clean energy projects. The study scrutinizes various mechanisms established from 2005 onwards, evaluating their efficacy in achieving the desired outcome of supporting clean energy project implementation. The aim is to comprehensively assess the associated costs, effectiveness, and attainment of renewable energy objectives stemming from implementing diverse support schemes. This paper explores the effectiveness of feed-in tariffs and feed-in premium mechanisms in promoting the integration of new renewable energy generation into the power system. However, the Spanish experience suggests that exhaustive planning control is imperative to prevent excessive support costs that could jeopardize the entire market and cause a significant rise in final energy costs. The research studies the support schemes and associated market distortion and presents the roadmap from regulated tariffs to a "total market" for wind and solar energy. The findings demonstrate that, under proper supervision and planning, a transitional retribution scheme effectively promotes renewable energy plant integration. Finally, the analysis examines the need to regulate the excess benefits that non-emitting power sources receive in a marginal price market, as this can lead to market imbalances and a substantial increase in final costs. These plans serve as a crucial tool in achieving the decarbonization objectives of the European Power System. [ABSTRACT FROM AUTHOR]

Wind power is one of the most important renewable energy sources worldwide. However, integrating these technologies in electricity markets faces challenges due to the intermittent wind speed, resulting in potential imbalances within power grids and subsequent inefficiencies. Traditional approaches for modeling the uncertainty simulate fixed wind power values in operational and planning models, which work well for the short term but are inadequate for medium-term decision-making due to lower forecasting accuracy and the inability to differentiate the imbalance risk faced in each simulated value. This paper proposes a novel methodology for incorporating wind energy imbalance risk into medium-term generation planning models. For this, we first simulate realistic wind power scenarios and perform a fitting distribution process to characterize the hourly wind power behavior. Then, we model the imbalance risk using quadratic-uncertainty cost functions for each simulated value. Subsequently, we conduct a temporal aggregation to reduce the computational burden, preserving the conceptual framework of the imbalance risk modeling. Finally, we propose a medium-term generation planning model that includes the imbalance risk model and the temporal aggregation strategy. Our approach is evaluated in a real-size case study formed by Spain, Portugal, and France's electricity markets, demonstrating accurate wind power imbalance risk modeling and lowered planning costs. • A new methodology to include wind energy imbalance risk in generation planning models. • Introducing a fitting process for modeling the distribution asymmetries of wind power. • Proposing a temporal aggregation method for efficient imbalance risk modeling. • The methodology is validated in Spanish, Portuguese, and French electricity markets. [ABSTRACT FROM AUTHOR]

Despite the relevant potential of offshore resource to substantially mitigate the effects of climate change through the generation of renewable electricity, the full exploitation of such wind resource currently remains incomplete. To promote offshore wind energy projects, it is crucial to engage in comprehensive planning processes that encompass technical, social, environmental, stakeholder, and political considerations. Within this framework, this paper presents a combined approach based on Geographical Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methodology to optimize the selection of offshore wind farm locations. The proposed methodology includes annual net electricity production as a critical factor in the decision-making process, which has been previously optimized with regard to the wake effect. Subsequently, the determination of potential locations involves key criteria such as technical feasibility, environmental impact, economic viability, and power generation potential. This MCDM-GIS approach was applied and evaluated in a case study along the Spanish coastline, considering 92 initial alternatives. The results indicate that the offshore wind energy targets established for Spain in 2030 and 2050 should be reevaluated, as the optimal utilization of the available area represents only 16% of the total potential area. Based on the aforementioned criteria, a suitability map was generated by integrating all relevant map layers, including their respective buffer zones. [Display omitted] • Net electricity production criterion included in the alternative decision matrix. • GIS-MCDM synergy methodology at territorial level to optimize offshore wind sites. • Multi-criteria definition includes technical, environmental, and economic factors. • 92 potential offshore wind plant alternatives in a Spanish case study are considered. • Results give different ranking of alternatives depending on each MCDM approach. [ABSTRACT FROM AUTHOR]

• 2050 low-carbon economy pledge and Spanish photovoltaic investment plan. • Photovoltaic spatial allocations minimising unpredictable variability. • Impact of wind energy investments on the optimal photovoltaic spatial allocation. • Allocation of photovoltaic and wind investments minimising supply–demand mismatch. • Assessment of the current photovoltaic investments in Spain. After a period of stagnation that lasted until approximately 2018, Spain has established an ambitious investment program in photovoltaics, derived from the pledge to switch to a carbon-free energy system by 2050, the sustained cost decreases of the technology, and the excellent irradiance values over its territory. This paper implements an additional criterion for the spatial allocation of these investments based on minimising the energy variability generated because of weather intermittency. The analysis is based on hourly data for the years 2005–2020 (inclusive), in various locations homogeneously spread throughout Spain. Subtracting the deterministic daily and annual irradiance cycles to estimate the variability of the random component is discussed. Failing to do that yields significantly higher and distorted values for solar energy variability, resulting in low investment proportions when combined with other energies, like wind. The first results show that a straightforward, equally-weighted allocation of investments is suboptimal. It is also shown that investing in low-irradiance locations contributes to reducing overall variability. Secondly, the study analyses overall power variability minimisation conditional on a given wind investment weight. It is found that the impact is significant spatially, and that proportions of wind energy above 10% increase aggregated variability across all levels of aggregated power generated. Nevertheless, investment proportions in wind energy below 10% reduce the overall combined variability significantly due to the negative correlation between wind and solar irradiation. The third point addressed is the minimisation of the mismatches between renewable energy supply and aggregate electricity demand. The optimal proportion for solar and wind investments becomes close to 50% in this case. However, this proportion tilts towards solar investments if variability minimisation is also considered. Finally, the current spatial distribution of photovoltaic investments in Spain is analysed, and it is shown that there is room for improvement. It is also found that the current ratio between wind and photovoltaic energy may not be optimal, and that it would be advisable to increase proportionally more photovoltaic energy. [ABSTRACT FROM AUTHOR]

In this paper, we explore two strategies for reducing the cash flow uncertainty of wind energy producers ascribed to variable weather conditions. The first strategy is based on the idea of aggregating the output of geographically-dispersed generating units. The second strategy employs financial instruments that compensate producers for unanticipated declines in the power delivery. Using a blend of advanced weather modeling, time series analysis, simulation and optimization techniques, we empirically access the ability of the two risk management approaches to control volumetric risk in Spain. With the aid of factor analysis techniques, we proceed with the decomposition of the remaining risk levels and assess the exposure of each strategy's revenue to systematic risk factors. Motivated by the results of this analysis, we propose new financial contracts and mixed-style strategies that are better suited to the risk position of each market player. • We introduce various strategies for managing the wind volumetric risk in Spain. • We quantify their ability to reduce the revenue variance of wind power producers. • Spatial aggregation of resources is not a cure for wind energy stochasticity. • Commercial wind power futures cannot hedge away all risk dimensions. • New contract designs are needed to control systematic variations in producers' income. [ABSTRACT FROM AUTHOR]

• A new hybrid prediction system is proposed to predict wind speed. • Simplex and chaotic mapping optimization cuckoo algorithm are proposed. • An uncertainty prediction model based on Monte Carlo method is proposed. • The energy theory is proposed to optimize the decomposition mode number. • Theories proofs and experiments verify the effectiveness of the model. Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the bridge-pilling modifications on the tidal flow at the strait of a human-altered mesotidal estuary (Cádiz Bay, Spain). The analysis was accomplished by combining (1) field data of current velocities in a cross-section close to the pile of a recently built bridge, and (2) hydrodynamics results from the calibrated and tested DELFT3D numerical model on the study site. The analysis was focused on bridge piling effects at intratidal scale during neap and spring tides. Semidiurnal (D2), quarti-diurnal (D4) and sixth-diurnal (D6) bands were examined. The ratio of quarter-diurnal to semidiurnal amplitudes (D4/D2) is largest (¿1) near the bottom of the channel due to friction, and the ratio D6/D2 is largest (¿1.5) close to the pile and in the shallowest part of the cross-section, due to internal asymmetry and tidal advective accelerations. Comparing neap and spring tides, the most marked asymmetries are found in the latter. These asymmetries occur around the wake of the pile, alternating from flood to ebb. The location of the bridge pile causes a decrease in the amplitude of D2 (50%), and a phase lag in the tidal wave of 30 min. The D6 amplitude increases up to almost 100% due to distortion of the tidal wave induce by the wake created at the pile. Moreover, the pile reduces about 1 m the depth where the pycnocline is observed. Additionally, the pile can be considered as an obstacle for the sediment flux. These effects can be extrapolated to similar infrastructures, such as wind or wave energy farms, that can potentially modify the estuarine dynamics. • Tidal flow is modified by a bridge piling in an estuary. • The M2 tidal amplitude is reduced up to 50% near the pile. • The pile causes a 30-min phase lag in the pile wake. • The sixth-diurnal tidal band increases up to 100%. • The center of the pycnocline is deepened by 1 m. [ABSTRACT FROM AUTHOR]

• A novel asexual-reproduction evolutionary neural network is proposed. • Three kinds of generative adversarial networks are applied to augment the training data. • The wind data are decomposed by the ensemble empirical mode decomposition technology. • The proposed approach outperforms much better than other SIA-based models. Accurate forecasts of wind power generation are essential for the operation of wind farms. But for the newly developed stations, it is difficult to make accurate prediction because there are no sufficient historical data available. It will thus be interesting to explore new data augmentation and prediction modeling approach adaptive to such new-built wind farms. In this regard, a novel asexual-reproduction evolutionary neural network (ARENN) for short-term wind power prediction based on Wasserstein generative adversarial network with gradient penalty (WGANGP) and ensemble empirical mode decomposition (EEMD) is presented in this paper. To solve the dilemma that new-built wind farms lack sufficient wind power data, the WGANGP is first applied to generate realistic data with a similar distribution of real data to augment the training dataset, which is further decomposed into a series of more stable subsequences by the EEMD so as to reduce the prediction difficulty of the machine learning model. In this study, a novel ARENN prediction model is developed to make the short-term wind power prediction, in which an asexual-reproduction evolutionary approach is first proposed to optimize the neural network based on a set of different loss functions that facilitate the population of network parameters approximating to the global optimum along different error surfaces in the evolutionary process. The proposed approach is validated on the data collected from the wind farm located in Spain and the predicted results demonstrate the advantage of our proposed approach over other methods involved in this study. [ABSTRACT FROM AUTHOR]