Renewable Energy

The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets ArXiv ID: 2511.05523 “View on arXiv” Authors: Ciaran O’Connor, Mohamed Bahloul, Steven Prestwich, Andrea Visentin Abstract Electricity price forecasting has become a critical tool for decision-making in energy markets, particularly as the increasing penetration of renewable energy introduces greater volatility and uncertainty. Historically, research in this field has been dominated by point forecasting methods, which provide single-value predictions but fail to quantify uncertainty. However, as power markets evolve due to renewable integration, smart grids, and regulatory changes, the need for probabilistic forecasting has become more pronounced, offering a more comprehensive approach to risk assessment and market participation. This paper presents a review of probabilistic forecasting methods, tracing their evolution from Bayesian and distribution based approaches, through quantile regression techniques, to recent developments in conformal prediction. Particular emphasis is placed on advancements in probabilistic forecasting, including validity-focused methods which address key limitations in uncertainty estimation. Additionally, this review extends beyond the Day-Ahead Market to include the Intra-Day and Balancing Markets, where forecasting challenges are intensified by higher temporal granularity and real-time operational constraints. We examine state of the art methodologies, key evaluation metrics, and ongoing challenges, such as forecast validity, model selection, and the absence of standardised benchmarks, providing researchers and practitioners with a comprehensive and timely resource for navigating the complexities of modern electricity markets. ...

Optimized Operation of Standalone Battery Energy Storage Systems in the Cross-Market Energy Arbitrage Business ArXiv ID: 2509.21337 “View on arXiv” Authors: Luis van Sandbergen Abstract The provision of renewable electricity is the foundation for a sustainable future. To achieve the goal of sustainable renewable energy, Battery Energy Storage Systems (BESS) could play a key role to counteract the intermittency of solar and wind generation power. In order to aid the system, the BESS can simply charge at low wholesale prices and discharge during high prices, which is also called energy arbitrage. However, the real-time execution of energy arbitrage is not straightforward for many companies due to the fundamentally different behavior of storages compared to conventional power plants. In this work, the optimized operation of standalone BESS in the cross-market energy arbitrage business is addressed by describing a generic framework for trading integrated BESS operation, the development of a suitable backtest engine and a specific optimization-based strategy formulation for cross-market optimized BESS operation. In addition, this strategy is tested in a case study with a sensitivity analysis to investigate the influence of forecast uncertainty. The results show that the proposed strategy allows an increment in revenues by taking advantage of the increasing market volatility. Furthermore, the sensitivity analysis shows the robustness of the proposed strategy, as only a moderate portion of revenues will be lost if real forecasts are adopted. ...

OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting ArXiv ID: 2502.06830 “View on arXiv” Authors: Unknown Abstract Probabilistic intraday electricity price forecasting is becoming increasingly important with the growth of renewable generation and the rise in demand-side engagement. Their uncertainties have increased the trading risks closer to delivery and the subsequent imbalance settlement costs. As a consequence, intraday trading has emerged to mitigate these risks. Unlike auction markets, intraday trading in many jurisdictions is characterized by the continuous posting of buy and sell orders on power exchange platforms. This dynamic orderbook microstructure of price formation presents special challenges for price forecasting. Conventional methods represent the orderbook via domain features aggregated from buy and sell trades, or by treating it as a multivariate time series, but such representations neglect the full buy-sell interaction structure of the orderbook. This research therefore develops a new order fusion methodology, which is an end-to-end and parameter-efficient probabilistic forecasting model that learns a full interaction-aware representation of the buy-sell dynamics. Furthermore, as quantile crossing is often a problem in probabilistic forecasting, this approach hierarchically estimates the quantiles with non-crossing constraints. Extensive experiments on the market price indices across high-liquidity (German) and low-liquidity (Austrian) markets demonstrate consistent improvements over conventional baselines, and ablation studies highlight the contributions of the main modeling components. The methodology is available at: https://runyao-yu.github.io/OrderFusion/. ...

Why Is GreenFinanceImportant? ArXiv ID: ssrn-3327149 “View on arXiv” Authors: Unknown Abstract In 2017, global investment in renewables and energy efficiency declined by 3% and there is a risk that it will slow further; clearly fossil fuels still dominate Keywords: Renewable Energy, Energy Efficiency, Green Investment, Energy Sector, Fossil Fuels, Infrastructure / Energy Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper relies on qualitative analysis, descriptive statistics, and policy discourse rather than advanced mathematics or rigorous backtesting. It discusses macroeconomic trends and policy recommendations without complex empirical modeling or implementation-heavy data analysis. flowchart TD A["Research Question: Why Is Green Finance Important?"] --> B["Data Analysis"] B --> C["Process Global Investment Trends"] C --> D["Compare Renewables vs Fossil Fuels"] D --> E{"Outcome: Fossil Fuels Still Dominate"} E --> F["Findings: 2017 Renewables Investment Dropped 3%"] E --> G["Implication: Risk of Further Investment Slowdown"]