Optimal Management and Design of Energy Systems under Atmospheric Uncertainty

The generation and distpatch of electricity while maintaining high reliability levels are two of the most daunting engineering problems of the modern era. This was demonstrated by the Northeast blackout of August 2003, which resulted in the loss of 6.2 gigawatts that served more than 50 million people and which resulted in economic losses on the order of $10 billion. In addition, there exist strong socioeconomic pressures to improve the efficiency of the grid. The most prominent solution to this problem is a substantial increase in the use of renewable energy such as wind and solar. In turn, its uncertain availability—which is due to the intrinsic weather variability—will increase the likelihood of disruptions. In this endeavors of current and next-generation power systems, forecasting atmospheric conditions with uncertainty can and will play a central role, at both the demand and the generation ends. User demands are strongly correlated to physical conditions such as temperature, humidity, and solar radiation. The reason is that the ambient temperature and solar radiation dictate the amount of air conditioning and lighting needed in residential and commercial buildings. But these potential benefits would come at the expense of increased variability in the dynamics of both production and demand, which would become even more dependent on weather state and its uncertainty. One of the important challenges for energy in our time is how to harness these benefits while “keeping the lights on”—ensuring that the demand is satisfied at all times and that no blackout occurs while all energy sources are optimally used. If we are to meet this challenge, accounting for uncertainty in the atmospheric conditions is essential, since this will allow minimizing the effects of false positives: committing too little baseline power in anticipation of demand that is underestimated or renewable energy levels that fail to materialize. In this work we describe a framework for the optimal management and design of energy systems, such as the power grid or building systems, under atmospheric conditions uncertainty. The framework is defined in terms of a mathematical paradigm called stochastic programming: minimization of the expected value of the decision-makers objective function subject to physical and operational constraints, such as low blackout porbability, that are enforced on each scenario. We report results on testing the framework on the optimal management of power grid systems under high wind penetration scenarios, a problem whose time horizon is in the order of days. We discuss the computational effort of scenario generation which involves running WRF at high spatio-temporal resolution dictated by the operational constraints as well as solving the optimal dispatch problem. We demonstrate that accounting for uncertainty in atmospheric conditions results in blackout prevention, whereas decisions using only mean forecast does not. We discuss issues in using the framework for planning problems, whose time horizon is of several decades and what requirements this problem would entail from climate simulation systems.