Optimal Subhourly Electricity Resource Dispatch Under Multiple Price Signals With High Renewable Generation Resource Availability
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Abstract not provided.
A method of managing thermal levels in a memory system may include determining an expected thermal level associated with each of a plurality of locations in a memory structure, and for each operation of a plurality of operations addressed to the memory structure, assigning the operation to a target location of the plurality of physical locations in the memory structure based on a thermal penalty associated with the operation and the expected thermal level associated with the target location.
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Increased contributions from wind and solar energy have helped set the United States on an attainable pathway towards carbon-free energy production. Though renewable energy is pivotal for this goal, saturating the grid with these variable energy sources has its challenges. Resources for wind and solar vary from factors beyond human control, hence the power output from these energy generators doesn’t necessarily match power demand for a specific region at a given moment in time. Solar photovoltaic production has a drastic mismatch since the peaks for power demand and power production are often out of phase: production peaks with available irradiance while demand peaks during the morning and evening hours. This “duck curve” in energy demand, the difference between high and low demand, grows as the grid becomes more saturated with renewable energy providers.
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With the increasing connectedness and interdependence of systems that are stochastic in nature, the issue of how to manage and coordinate them for safe operation has evidently become more important. In many networked system architectures, the system-wide output has to be delicately managed; often within a prescribed set of bounds. In this paper, a novel control framework is proposed where the bounds on the outputs are translated into independent bounds on the controllable inputs of each subsystem. The main benefit of this framework is that respecting the individual control bounds suffices to guarantee that the system-wide outputs will remain within safe boundaries. Since the systems are assumed to be stochastic, the bounds on the output are introduced as probabilistic chance constraints. The benefits of this framework are demonstrated by applying it to the control of distributed energy resources in a distribution networks where main goal is to keep the voltage magnitudes with their prescribed bounds. The control bounds are evaluated using real data on an IEEE test system.
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The cost of hydrogen production is projected to decline over the next several decades alongside the widespread deployment of energy constrained generating resources and electrification. While today’s system can utilize fast responding thermal generating assets to answer to changes in demand and energy constrained resource output, the system of tomorrow will more likely rely upon price responsive demands and virtual power plants. Since hydrogen is anticipated to be a primary generating fuel of the future while also providing a large demand base for its production, it is reasonable to evaluate the potential performance for hydrogen-electric coordination issues.
Flexible loads like smart thermostats and water heaters can shift energy consumption and provide flexibility to the grid. However, this flexibility is dependent on occupant behavior and can lead to delivery risk, which causes utilities and grid operations to consider them as unreliable for purposes of grid operation. To date, they have not been well integrated into wholesale electricity markets or ancillary service offerings. With proper consideration of uncertainty and risk, these resources can be one of the most cost-effective sources of flexibility. This work uses stochastic optimization to quantify and bid flexibility from a fleet of flexible resources while considering their delivery risk.