A sharing economy for residential communities with PV-coupled battery storage: Benefits, pricing and participant matching

Henni, Sarah; Staudt, Philipp; Weinhardt, Christof

doi:10.1016/j.apenergy.2021.117351

Cited by 53 publications

(17 citation statements)

References 25 publications

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“…ARIMA, widely employed for various forecasting challenges such as electricity price and demand prediction, exemplifies this approach. [9] introduced a novel methodology based on wavelet decomposition of multivariate time series, allowing for the breakdown of complex data into simpler components for modeling, with subsequent reconstruction for final output. Similarly, [10] proposed a modification to the traditional ARIMA method by dividing the dataset into training and test sets, applying the model separately, and then integrating the results for improved accuracy.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation

Mandadapu

2024

Preprint

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Electricity price forecasting has become increasingly vital following the liberalization of electricity markets—ushering in a more competitive environment for electricity generation and distribution. Notably, electricity prices in Day-Ahead Markets (DAMs) hold significant sway, influencing decisions made by energy traders. However, modeling electricity prices poses challenges due to their inherent characteristics such as heteroscedasticity, sharp price spikes, and multiple levels of seasonality. Therefore, in this study, we delve into various methodologies from existing literature to forecast electricity spot prices within the Irish DAM. Our focus lies on employing time series and Machine Learning (ML) techniques to predict prices for all 24 hours of each DAM auction facilitated by the Single Electricity Market Operator (SEMO). For this, we begin by providing a concise overview of the electricity market and its functioning, particularly concerning our objective of price forecasting within the DAM. Subsequently, we elucidate the key aspects of the data utilized in this study. Following this, we offer succinct explanations of each model employed, detailing their structures and preparatory steps for the modeling task. Central to our analysis are the results showcasing the performance of each model relative to a benchmark, along with a brief discussion on the significance of predictors in the forecasting process. Finally, based on our findings, we draw conclusions and outline potential avenues for further research and development.

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Section: Related Workmentioning

confidence: 99%

Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation

Mandadapu

2024

Preprint

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“…This has the great advantage of achieving precise models of a distributed system taking into account its numerous degrees of freedom; however, this approach also brings the disadvantage of adding complexity to the models (together with a high computational cost), which prevents drawing conclusions about the properties of these models. In fact, many papers achieve the goal of predicting with high certainty the positive impact of the management strategies in specific collaborative systems; however, some authors, such as Henni, Staudt and Weinhardt (2021), point out that the individual impact of the benefits over each one of the participants has not been sufficiently analyzed in the literature.…”

Section: Main Research Trendsmentioning

confidence: 99%

A simple and efficient method to allocate costs and benefits in energy communities

González-Asenjo

Izquierdo

Sedano³

2023

JIEM

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Purpose: Define a simple and efficient method to allocate costs and benefits in energy communities, and characterize some of its key properties.Design/methodology/approach: The approach is theoretical. We define an algorithm to allocate costs and benefits in energy communities, and derive some of its formal properties using mathematical reasoning. We also compare the proposed algorithm with several alternatives.Findings: The proposed algorithm is simple and it ensures that the resulting distribution of costs and benefits is (i) beneficial for every member of the community, (ii) efficient, (iii) fair (in a formally defined sense), (iv) smooth (small changes in the consumption or in the generation of energy cannot lead to big changes in the allocation of costs and benefits), and (v) environmentally friendly in the sense that the individual allocated cost is a strictly increasing function of individual consumption.Research limitations/implications: The properties of the proposed algorithm are satisfied for a specific type of energy community that is defined in the manuscript. Practical implications: The algorithm is easy to implement in any energy community.Social implications: The algorithm is highly relevant for any community of prosumers who are willing to exchange energy internally. It guarantees a number of desirable properties that are formally defined in the paper.Originality/value: We prove that a simple algorithm to allocate costs and benefits in energy communities guarantees the fulfilment of several desirable properties.

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“…Nonetheless, this increasing trend might not be enough to accomplish Sustainable Development Goal 7 [32]. Cost-wise, PV and wind systems' deployment capital and operational expenses have decreased due to technological improvements, manufacturing processes, and incentives from tax authorities in multiple countries [33].…”

Section: Renewable Energy Sourcesmentioning

confidence: 99%

Assessing the Role of Energy Storage in Multiple Energy Carriers toward Providing Ancillary Services: A Review

2022

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Renewable energy power plants and transport and heating electrification projects are being deployed to enable the replacement of fossil fuels as the primary energy source. This transition encourages distributed generation but makes the grid more weather-dependent, thus reducing its inertia. Simultaneously, electrical network operators face voltage, frequency, and stability challenges at the distribution level. Networks were not designed to manage the stochasticity of renewable energy sources or the congestion caused by the new transport and heating demands. Such challenges are commonly addressed through infrastructure reinforcements. This review studies how energy storage systems with different carriers can provide a collaborative solution involving prosumers as ancillary services providers at the distribution level. We focused on the European urban context; thus, we analyzed renewable energy sources, batteries, supercapacitors, hydrogen fuel cells, thermal energy storage, and electric vehicles. A thorough review of successful implementations proved that including storage in one or more carriers benefits the distribution system operators and the prosumers, from both technical and economic perspectives. We propose a correlation between individual energy storage technologies and the ancillary services they can provide based on their responses to specific grid requirements. Therefore, distribution system operators can address network issues together with the prosumers. Nevertheless, attractive regulatory frameworks and business models are required to motivate prosumers to use their assets to support the grid. Further work is recommended to describe the joint operation of multiple storage technologies as multicarrier systems, focusing on the coupling of electrical and thermal energy storage. Additionally, how ancillary services affect the energy storage system’s aging should be studied.

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A sharing economy for residential communities with PV-coupled battery storage: Benefits, pricing and participant matching

Cited by 53 publications

References 25 publications

Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation

Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation

A simple and efficient method to allocate costs and benefits in energy communities

Assessing the Role of Energy Storage in Multiple Energy Carriers toward Providing Ancillary Services: A Review

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