The energy transition is profoundly reshaping electricity market dynamics. It makes it essential to understand how renewable energy generation actually impacts electricity prices, among all other market drivers. These insights are critical to design policies and market interventions that ensure affordable, reliable, and sustainable energy systems. However, identifying causal effects from observational data is a major challenge, requiring innovative causal inference approaches that go beyond conventional regression analysis only. We build upon the state of the art by developing and applying a local partially linear double machine learning approach. Its application yields the first robust causal evidence on the distinct and non-linear effects of wind and solar power generation on UK wholesale electricity prices, revealing key insights that have eluded previous analyses. We find that, over 2018-2024, wind power generation has a U-shaped effect on prices: at low penetration levels, a 1 GWh increase in energy generation reduces prices by up to 7 GBP/MWh, but this effect gets close to none at mid-penetration levels (20-30%) before intensifying again. Solar power places substantial downward pressure on prices at very low penetration levels (up to 9 GBP/MWh per 1 GWh increase in energy generation), though its impact weakens quite rapidly. We also uncover a critical trend where the price-reducing effects of both wind and solar power have become more pronounced over time (from 2018 to 2024), highlighting their growing influence on electricity markets amid rising penetration. Our study provides both novel analysis approaches and actionable insights to guide policymakers in appraising the way renewables impact electricity markets.

Accurate generation of realistic future scenarios of renewable energy generation is crucial for long-term planning and operation of electrical systems, especially considering the increasing focus on sustainable energy and the growing penetration of renewable generation in energy matrices. These predictions enable power system operators and energy planners to effectively manage the variability and intermittency associated with renewable generation, allowing for better grid stability, improved energy management, and enhanced decision-making processes. In this paper, we propose an innovative method for generating long-term hourly scenarios for wind and solar power generation, taking into consideration the correlation between these two energy sources. To achieve this, we combine the capabilities of a Variational Autoencoder (VAE) with the additional benefits of incorporating the Radial Basis Function (RBF) kernel in our artificial neural network architecture. By incorporating them, we aim to obtain a latent space with improved regularization properties. To evaluate the effectiveness of our proposed method, we conduct experiments in a representative study scenario, utilizing real-world wind and solar power generation data from the Brazil system. We compare the scenarios generated by our model with the observed data and with other sets of scenarios produced by a conventional VAE architecture. Our experimental results demonstrate that the proposed method can generate long-term hourly scenarios for wind and solar power generation that are highly correlated, accurately capturing the temporal and spatial characteristics of these energy sources. Taking advantage of the benefits of RBF in obtaining a well-regularized latent space, our approach offers improved accuracy and robustness in generating long-term hourly scenarios for renewable energy generation.

The need for a rapid transition to clean energy is enabling new developments in the renewable sector. Businesses and industries are moving towards renewable energy to reduce emissions, lower energy costs, and improve eco-friendliness. The major trends in the renewable sector include digitization, energy-efficient integrations, and solutions that overcome the intermittency in renewable energy production. For these reasons, the use of big data, artificial intelligence (AI), and the internet of energy (IoE) are emerging as popular trends in addition to innovations in renewable energy sources. Although renewable energies such as solar, wind, and hydroelectricity have been around for a long time, recent rapid innovations make these some of the most trending technologies. Moreover, they dominate the industry due to their competitive advantages. Relatively newer areas of research in the renewable sector include energy from green hydrogen and water energy forms such as tidal, wave, and ocean currents. For this in-depth research on the Top Renewable Energy Trends & Startups, we analyzed a sample of 5 152 global startups and scaleups. The result of this research is data-driven innovation intelligence that improves strategic decision-making by giving you an overview of emerging technologies & startups in the renewable energy industry. These insights are derived by working with our Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform, covering 2 093 000 startups & scaleups globally.

The "Innovations in Renewable Energy Generation, Desalination, Artificial Intelligence, LEDs, and Vaccines" report has been added to ResearchAndMarkets.com's offering. This edition of the Inside R&D TechVision Opportunity Engine (TOE) features an innovation for enhancing digital imaging in deep learning and an innovation based on using novel receptors for mitigating vector borne diseases. The TOE also provides intelligence on the efficient conversion of carbon dioxide in to value added products and the use of passive solar power for desalination. The TOE also features innovations based on the use of sustainable materials for oil water separation and environment friendly materials that can be used in the construction industry. The TOE additionally provides insights on numerous AI-based solutions for detection of cyber attacks, accurate assessment of diseases, and for the improvement of haptic feedback during telerobotic surgeries.

As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.