Optimization of the planning and operations of electric distribution grids in the context of high renewable energy penetration

Abstract : In the context of the energy transition, there are many unknowns related to the required capabilities of future electric distribution systems to meet the growing electric load and new forms of electric production. The transformation of current electric distribution systems is inevitable, however, the most cost-effective investments are difficult to evaluate. Current electric distribution grid planning strategies are inadequate to take into account the accommodation of massive decentralized production, increased electric load with higher volatility, automation of distribution grids and unbundling of electricity markets. Due to a lack of observability and controllability in the distribution grid, the feasibility of optimal power flow management is not currently a reality. The quantification of smart distribution grids is critical to evaluate the added benefit of this solution in comparison to infrastructure upgrades.The primary objective of my PhD is to explore the techno-economical barriers of massive renewable energy integration into the distribution grid. This thesis will explore different solutions through convex relaxations of optimal power flow analysis. For the low voltage distribution grid case, three-phase unbalanced power flow analysis is considered. In order to consider realistically the uncertainties related to renewable generation and demand, stochastic optimal power flow (OPF) algorithms are proposed. These tools are used among others to i) optimize placement and sizing of grid connected storage, ii) optimize demand response strategies, iii) study different operation strategies for storage devices including centralized and decentralized ones and iv) study the impact of different renewable energy integration scenarios into real-world distribution grids.
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Etta Grover Silva. Optimization of the planning and operations of electric distribution grids in the context of high renewable energy penetration. Electric power. PSL Research University, 2017. English. ⟨NNT : 2017PSLEM074⟩. ⟨tel-01899752⟩

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