arXiv subjects

Simon Camal

Publications and source records attributed to Simon Camal.

5 recordsLinked to original sources

arXiv2025

A Scenario-Spatial Decomposition Approach With a Performance Guarantee for the Combined Bidding of Cascaded Hydropower and Renewables

This study develops a scalable co-optimization strategy for the joint bidding of cascaded hydropower, wind, and solar energy units, treated as a unified entity in the day-ahead market. Although hydropower flexibility can manage the stochasticity of renewable energy, the underlying bidding problem is complex due to intricate coupling constraints and nonlinear dynamics. A decomposition in both scenario and spatial dimensions is proposed, enabling the use of distributed optimization. The proposed distributed algorithm is eventually a heuristic due to non-convexities arising from the system's physical dynamics. To ensure a performance guarantee, trustworthy upper and lower bounds on the global optimum are derived, and a mathematical proof is provided to demonstrate their existence and validity. This approach reduces the average runtime by up to 35% compared to alternative distributed methods and by 57% compared to the centralized optimization. Moreover, it consistently delivers solutions, whereas both centralized and alternative distributed approaches fail as the size of the optimization problem grows.

math.OC↗

arXiv2024

Hedging Hydrogen: Planning and Contracting Under Uncertainty for a Green Hydrogen Producer

Green hydrogen production by water electrolysis using renewable electricity is considered essential for decarbonisation of certain sectors of the global economy, however development of the industry is lagging behind expectations due to the perceived financial risk for individual projects. This risk stems from a number of uncertainties, including future hydrogen demand, variable renewable energy sources, and volatile energy market prices. The interaction of these uncertainties is complex, yet the analysis of hydrogen projects is often carried out using simplified modelling that often omits uncertainty and/or energy hedging practices which are typical for intensive power consumers. In this study, we define a set of planning methods (planning policies) in order to compare the effectiveness of different modelling approaches. We propose a 2-stage market-focused stochastic program to represent a hydrogen producer supplying an industrial customer through a hydrogen offtake contract (a Hydrogen Purchase Agreement, or HPA). The model can be used to obtain equipment sizing decisions, as well as energy hedging decisions using Power Purchase Agreements (PPA's) and power futures. We find that for some HPA contract types, failure to use stochastic modelling can lead to planning decisions that result in 30% higher production costs during scenario stress-testing for the same project. This could lead to some projects being discarded by developers, incorrectly deemed to be unviable due to cost projections being too high. The results also show the importance of HPA contract volumetric obligations in limiting demand uncertainty.

math.OC↗

arXiv2024

News and Load: Social and Economic Drivers of Regional Multi-horizon Electricity Demand Forecasting

The relationship between electricity demand and variables such as economic activity and weather patterns is well established. However, this paper explores the connection between electricity demand and social aspects. It further embeds dynamic information about the state of society into energy demand modelling and forecasting approaches. Through the use of natural language processing on a large news corpus, we highlight this important link. This study is conducted in five regions of the UK and Ireland and considers multiple time horizons from 1 to 30 days. It also considers economic variables such as GDP, unemployment and inflation. The textual features used in this study represent central constructs from the word frequencies, topics, word embeddings extracted from the news. The findings indicate that: 1) the textual features are related to various contents, such as military conflicts, transportation, the global pandemic, regional economics, and the international energy market. They exhibit causal relationships with regional electricity demand, which are validated using Granger causality and Double Machine Learning methods. 2) Economic indicators play a more important role in the East Midlands and Northern Ireland, while social indicators are more influential in the West Midlands and the South West of England. 3) The use of these factors improves deterministic forecasting by around 6%.

cs.CL↗

arXiv2024

News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand

The relationship between electricity demand and weather is well established in power systems, along with the importance of behavioral and social aspects such as holidays and significant events. This study explores the link between electricity demand and more nuanced information about social events. This is done using mature Natural Language Processing (NLP) and demand forecasting techniques. The results indicate that day-ahead forecasts are improved by textual features such as word frequencies, public sentiments, topic distributions, and word embeddings. The social events contained in these features include global pandemics, politics, international conflicts, transportation, etc. Causality effects and correlations are discussed to propose explanations for the mechanisms behind the links highlighted. This study is believed to bring a new perspective to traditional electricity demand analysis. It confirms the feasibility of improving forecasts from unstructured text, with potential consequences for sociology and economics.

cs.CL↗

arXiv2023

Electricity Demand Forecasting through Natural Language Processing with Long Short-Term Memory Networks

Electricity demand forecasting is a well established research field. Usually this task is performed considering historical loads, weather forecasts, calendar information and known major events. Recently attention has been given on the possible use of new sources of information from textual news in order to improve the performance of these predictions. This paper proposes a Long and Short-Term Memory (LSTM) network incorporating textual news features that successfully predicts the deterministic and probabilistic tasks of the UK national electricity demand. The study finds that public sentiment and word vector representations related to transport and geopolitics have time-continuity effects on electricity demand. The experimental results show that the LSTM with textual features improves by more than 3% compared to the pure LSTM benchmark and by close to 10% over the official benchmark. Furthermore, the proposed model effectively reduces forecasting uncertainty by narrowing the confidence interval and bringing the forecast distribution closer to the truth.

cs.LG↗