Abstract
This research presents an example of the energy transition process application on conventional French urban communities and particularly, its possible integration into the Urban Communities Smart Grids (UCSG). For this, it was chosen one of typical urban community - Alfortville (94140), Paris region, France. The aim is to define all UCSG components and the adapted UCSG structure for the further application of the efficient, renewable, economic and resilient Day-Ahead Optimal Power Flow (DA-OPF) energy management, giving the opportunity to get additional distribution grid flexibility. The integration of UCSG’s DA-OPF management requires the centralized control and involves the integration of centralized battery storage systems (CESS) and distributed PV generation. It was determined the optimal penetration rate and size of distributed PV and CESS. The final considered UCSG simplified scheme and its components was defined. The efficient DA-OPF management strategy was applied on the obtained community scheme. The DA–OPF is based on a data forecast system that uses a Deep Learning (DL) Long Short-term Memory network (LSTM) and is formulated as a mathematical Mixed-Integer Nonlinear Programming (MINLP) model. The real data simulation UCSG showed significant benefits and an electricity price reduction for the considered urban community compared to a conventional case, as well as the easy applicability of proposed method. The efficiency and versatility of this research allow its easy application to others similar urban communities under UCSG integration.
Highlights
The energy transition and its socio-economic dynamic create significant challenges to be solved, in particular on the modes of production, storage and energy consumption
The proposed system iteratively combines Long Short-Term Memory (LSTM) deep learning algorithms and Optimal Power Flow (OPF) methods based on Mixed Integer NonLinear Programming (MINLP) presented in the previous research [1]
The efficiency and versatility of this research allow its easy application to others similar urban communities under Urban Communities Smart Grids (UCSG) integration
Summary
The energy transition and its socio-economic dynamic create significant challenges to be solved, in particular on the modes of production, storage and energy consumption. The significant increase of the penetration rate of Renewable Energies (RE) presents a promising path, notably through the development of distributed production and microgrids. In this case, national electric grids need much more manoeuvring capacity (flexibility) to offset these effects linked to the penetration of renewable Distributed Energy Resources (DER) and their intermittency. Flexibility management is achieved through the Day-Ahead Optimal Power Flow (DA-OPF) efficient management. The proposed system iteratively combines Long Short-Term Memory (LSTM) deep learning algorithms and Optimal Power Flow (OPF) methods based on Mixed Integer NonLinear Programming (MINLP) presented in the previous research [1]
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