8,530 research outputs found
Emission-aware Energy Storage Scheduling for a Greener Grid
Reducing our reliance on carbon-intensive energy sources is vital for
reducing the carbon footprint of the electric grid. Although the grid is seeing
increasing deployments of clean, renewable sources of energy, a significant
portion of the grid demand is still met using traditional carbon-intensive
energy sources. In this paper, we study the problem of using energy storage
deployed in the grid to reduce the grid's carbon emissions. While energy
storage has previously been used for grid optimizations such as peak shaving
and smoothing intermittent sources, our insight is to use distributed storage
to enable utilities to reduce their reliance on their less efficient and most
carbon-intensive power plants and thereby reduce their overall emission
footprint. We formulate the problem of emission-aware scheduling of distributed
energy storage as an optimization problem, and use a robust optimization
approach that is well-suited for handling the uncertainty in load predictions,
especially in the presence of intermittent renewables such as solar and wind.
We evaluate our approach using a state of the art neural network load
forecasting technique and real load traces from a distribution grid with 1,341
homes. Our results show a reduction of >0.5 million kg in annual carbon
emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the
ACM International Conference on Future Energy Systems (e-Energy 20) June
2020, Australi
Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems
Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management
Buildings-to-Grid Integration Framework
This paper puts forth a mathematical framework for Buildings-to-Grid (BtG)
integration in smart cities. The framework explicitly couples power grid and
building's control actions and operational decisions, and can be utilized by
buildings and power grids operators to simultaneously optimize their
performance. Simplified dynamics of building clusters and building-integrated
power networks with algebraic equations are presented---both operating at
different time-scales. A model predictive control (MPC)-based algorithm that
formulates the BtG integration and accounts for the time-scale discrepancy is
developed. The formulation captures dynamic and algebraic power flow
constraints of power networks and is shown to be numerically advantageous. The
paper analytically establishes that the BtG integration yields a reduced total
system cost in comparison with decoupled designs where grid and building
operators determine their controls separately. The developed framework is
tested on standard power networks that include thousands of buildings modeled
using industrial data. Case studies demonstrate building energy savings and
significant frequency regulation, while these findings carry over in network
simulations with nonlinear power flows and mismatch in building model
parameters. Finally, simulations indicate that the performance does not
significantly worsen when there is uncertainty in the forecasted weather and
base load conditions.Comment: In Press, IEEE Transactions on Smart Gri
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