Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

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Peer-to-peer energy trading and next generation local energy market mechanisms are expected to provide new use cases and opportunities within the future sharing economy landscape. To this anticipation, we propose alternative incentive mechanisms as energy policy instruments that can be used by policy makers for directly supporting local energy producers, and hence indirectly the consumers, at current local energy markets using capabilities provided by contemporary distributed ledger technology. Under such peer-topeer local market setting, we first detail market pricing and relevant market parameters thoroughly, and then we discuss fair incentive distribution to local producers in detail, by means of two distinct incentive systems what we call as the fixed stipend and the decaying stipend incentive mechanisms, respectively. We provide an analysis of market pricing and market parameters under German power market conditions, and an illustration of proposed support instruments with resorting to three scenarios experimented on a local energy market test bed that is equipped with realistic energy generation and consumption profiles for its participants. INDEX TERMS Blockchain technology, distributed ledger technology, energy policy, fair incentive mechanisms, local energy markets, local market pricing, peer-to-peer energy trading, sharing economy.

Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a "black-box" due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This paper presents several use cases of solar PV energy forecasting using XAI tools, such as LIME, SHAP, and ELI5, which can contribute to adopting XAI tools for smart grid applications. Understanding the inner workings of a prediction model based on AI can give insights into the application field. Such insight can provide improvements to the solar PV forecasting models and point out relevant parameters.INDEX TERMS Explainable Artificial Intelligence (XAI), solar PV power generation forecasting, explainability and transparency.