Transmission expansion planning (TEP) plays an important role in the integration of renewable energy sources (RES). In fact, the standards of expansion plans have significantly changed due to the speedily increase renewable penetration. In addition, stronger security requirements have been imposed in order to avoid blackouts associated with outages of system elements. In this paper, we propose a methodology to devise the optimal expansion plan while taking into account the possibility of system failures and uncertainty in renewable output. Since the uncertainty characterization of RES output still represents a challenge for operators and planners, the methodology proposed is general enough to accommodate a set of eligible RES probability distributions. Structurally, the proposed TEP formulation is a particular instance of the risk-constrained adjustable robust optimization (R-ARO) framework, modelled as a (hierarchical) three-level system of optimization problems. An IEEE-based test system is used to illustrate the effectiveness of the TEP model.
BT - 2019 IEEE Milan PowerTech2019 IEEE Milan PowerTech CY - Milan, Italy DO - 10.1109/PTC.2019.8810864 LA - eng N2 -Transmission expansion planning (TEP) plays an important role in the integration of renewable energy sources (RES). In fact, the standards of expansion plans have significantly changed due to the speedily increase renewable penetration. In addition, stronger security requirements have been imposed in order to avoid blackouts associated with outages of system elements. In this paper, we propose a methodology to devise the optimal expansion plan while taking into account the possibility of system failures and uncertainty in renewable output. Since the uncertainty characterization of RES output still represents a challenge for operators and planners, the methodology proposed is general enough to accommodate a set of eligible RES probability distributions. Structurally, the proposed TEP formulation is a particular instance of the risk-constrained adjustable robust optimization (R-ARO) framework, modelled as a (hierarchical) three-level system of optimization problems. An IEEE-based test system is used to illustrate the effectiveness of the TEP model.
PB - IEEE PP - Milan, Italy PY - 2019 T2 - 2019 IEEE Milan PowerTech2019 IEEE Milan PowerTech T3 - 2019 IEEE Milan PowerTech2019 IEEE Milan PowerTech TI - An ambiguity averse approach for transmission expansion planning UR - https://ieeexplore.ieee.org/document/8810864/ ER -