arXiv subjects

Julian Lemos-Vinasco

Publications and source records attributed to Julian Lemos-Vinasco.

5 recordsLinked to original sources

arXiv2025

A New Time Series Similarity Measure and Its Smart Grid Applications

Many smart grid applications involve data mining, clustering, classification, identification, and anomaly detection, among others. These applications primarily depend on the measurement of similarity, which is the distance between different time series or subsequences of a time series. The commonly used time series distance measures, namely Euclidean Distance (ED) and Dynamic Time Warping (DTW), do not quantify the flexible nature of electricity usage data in terms of temporal dynamics. As a result, there is a need for a new distance measure that can quantify both the amplitude and temporal changes of electricity time series for smart grid applications, e.g., demand response and load profiling. This paper introduces a novel distance measure to compare electricity usage patterns. The method consists of two phases that quantify the effort required to reshape one time series into another, considering both amplitude and temporal changes. The proposed method is evaluated against ED and DTW using real-world data in three smart grid applications. Overall, the proposed measure outperforms ED and DTW in accurately identifying the best load scheduling strategy, anomalous days with irregular electricity usage, and determining electricity users' behind-the-meter (BTM) equipment.

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arXiv2024

Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes

Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal use of network assets. Yet, existing DR models often overlook a crucial element, the irrational behaviour of electricity end users. In this work, we propose a price-responsive model that incorporates key aspects of end-user irrationality, specifically loss aversion, time inconsistency, and bounded rationality. To this end, we first develop a framework that uses Multiple Seasonal-Trend decomposition using Loess (MSTL) and non-stationary Gaussian processes to model the randomness in the electricity consumption by residential consumers. The impact of this model is then evaluated through a community battery storage (CBS) business model. Additionally, we apply a chance-constrained optimisation model for CBS operation that deals with the unpredictability of the end-user irrationality. Our simulations using real-world data show that the proposed DR model provides a more realistic estimate of end-user price-responsive behaviour when considering irrationality. Compared to a deterministic model that cannot fully take into account the irrational behaviour of end users, the chance-constrained CBS operation model yields an additional 19% revenue. Lastly, the business model reduces the electricity costs of solar end users by 11%.

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arXiv2023

On the Financial Consequences of Simplified Battery Sizing Models without Considering Operational Details

Optimal battery sizing studies tend to overly simplify the practical aspects of battery operation within the battery sizing framework. Such assumptions may lead to a suboptimal battery capacity, resulting in significant financial losses for a battery project that could last more than a decade. In this paper, we compare the most common existing sizing methods in the literature with a battery sizing model that incorporates the practical operation of a battery, that is, receding horizon operation. Consequently, we quantify the financial losses caused by the suboptimal capacities obtained by these models for a realistic case study related to community battery storage (CBS). We develop the case study by constructing a mathematical framework for the CBS and local end users. Our results show that existing sizing methods can lead to financial losses of up to 22%.

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arXiv2022

onlineforecast: An R package for adaptive and recursive forecasting

Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using neural network methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.

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arXiv2022

Economic evaluation of stochastic home energy management systems in a realistic rolling horizon setting

Home energy management systems (HEMSs) are expected to become a crucial part of future smart grids. However, there is a limited number of studies that comprehensively assess the potential economic benefits of HEMS for consumers under real market conditions and which take account of consumers' capabilities. In this study, a new optimization-based HEMS controller is presented to operate a photovoltaic and battery system. The HEMS controller considers the consumers' electrical load uncertainty by integrating multivariate probabilistic forecasting methods and a stochastic optimization in a rolling horizon. As a case study, a comprehensive simulation study is designed to emulate the operation of a real HEMS using real data from nine Danish homes over different seasons under real-time retail prices. The optimization-based control strategies are compared with a default (naive) control strategy that encourages self consumption. Simulation results show that seasonality in the consumers' load and electricity prices have a significant impact on the performance of the control strategies. A combination of optimization-based and naive control strategy presents the best overall results.

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