Quantifiably secure power grid operation, management, and evolution : a study of uncertainties affecting the grid integration of renewables.

This report summarizes findings and results of the Quantifiably Secure Power Grid Operation, Management, and Evolution LDRD. The focus of the LDRD was to develop decisionsupport technologies to enable rational and quantifiable risk management for two key grid operational timescales: scheduling (day-ahead) and planning (month-to-year-ahead). Risk or resiliency metrics are foundational in this effort. The 2003 Northeast Blackout investigative report stressed the criticality of enforceable metrics for system resiliency - the grid's ability to satisfy demands subject to perturbation. However, we neither have well-defined risk metrics for addressing the pervasive uncertainties in a renewable energy era, nor decision-support tools for their enforcement, which severely impacts efforts to rationally improve grid security. For day-ahead unit commitment, decision-support tools must account for topological security constraints, loss-of-load (economic) costs, and supply and demand variability - especially given high renewables penetration. For long-term planning, transmission and generation expansion must ensure realized demand is satisfied for various projected technological, climate, and growth scenarios. The decision-support tools investigated in this project paid particular attention to tailoriented risk metrics for explicitly addressing high-consequence events. Historically, decisionsupport tools for the grid consider expected cost minimization, largely ignoring risk and instead penalizing loss-of-load through artificial parameters. The technical focus of this work was the development of scalable solvers for enforcing risk metrics. Advanced stochastic programming solvers were developed to address generation and transmission expansion and unit commitment, minimizing cost subject to pre-specified risk thresholds. Particular attention was paid to renewables where security critically depends on production and demand prediction accuracy. To address this concern, powerful filtering techniques for spatio-temporal measurement assimilation were used to develop short-term predictive stochastic models. To achieve uncertaintytolerant solutions, very large numbers of scenarios must be simultaneously considered. One focus of this work was investigating ways of reasonably reducing this number