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• W3041716041 abstract "Increasing popularity of renewable and Distributed Energy Resources (DER) and introduction of smart meters are changing the way electricity distribution grids have been operated. The stochastic nature of renewable sources adds new challenges to distribution grid operations. Communities, which are defined as groups of individual customers that utilise renewable energy sources, are especially impacted by these challenges due to their lack of scale and know-how. In this thesis, we focus on PV-rich communities that have a number of end-users equipped with rooftop photovoltaic (PV) panels without any local storage. For such PV-rich communities, it would be beneficial to model and analyse the statistical properties of DERs and their demand. Historical data can help understand the stochastic behaviour of community DER and demand, and model them as random sequences. These random sequences are used as a basis for optimal decision-making on financial contracts between communities and energy generators. Unlike stochastic optimisation, forecasting, and the Monte Carlo simulation, our methodology enables PV-rich communities to conduct long-term planning, spot-market exposure risk analysis, fine-tuning power purchase agreements, and a good understanding of statistical properties of distribution networks utilising PV systems. Our approach benefits from data science and uses models and existing data in a computationally efficient manner. With the help of our proposed model-based tool, communities are able to plan their long term financial agreements without conducting a high number of simulations." @default.
• W3041716041 created "2020-07-16" @default.
• W3041716041 creator A5011754316 @default.
• W3041716041 date "2019-01-01" @default.
• W3041716041 modified "2023-09-26" @default.
• W3041716041 title "Probabilistic Energy Management Systems in PV-Rich Communities" @default.
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• W3041716041 hasPublicationYear "2019" @default.
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