1,667 research outputs found
Online Energy Generation Scheduling for Microgrids with Intermittent Energy Sources and Co-Generation
Microgrids represent an emerging paradigm of future electric power systems
that can utilize both distributed and centralized generations. Two recent
trends in microgrids are the integration of local renewable energy sources
(such as wind farms) and the use of co-generation (i.e., to supply both
electricity and heat). However, these trends also bring unprecedented
challenges to the design of intelligent control strategies for microgrids.
Traditional generation scheduling paradigms rely on perfect prediction of
future electricity supply and demand. They are no longer applicable to
microgrids with unpredictable renewable energy supply and with co-generation
(that needs to consider both electricity and heat demand). In this paper, we
study online algorithms for the microgrid generation scheduling problem with
intermittent renewable energy sources and co-generation, with the goal of
maximizing the cost-savings with local generation. Based on the insights from
the structure of the offline optimal solution, we propose a class of
competitive online algorithms, called CHASE (Competitive Heuristic Algorithm
for Scheduling Energy-generation), that track the offline optimal in an online
fashion. Under typical settings, we show that CHASE achieves the best
competitive ratio among all deterministic online algorithms, and the ratio is
no larger than a small constant 3.Comment: 26 pages, 13 figures. It will appear in Proc. of ACM SIGMETRICS, 201
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
Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting
Energy efficiency and consumers' role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers' power converters track the power setpoints obtained from the proposed strategy.Spanish Agencia Estatal de Investigacion ; Fondo Europeo de Desarrollo Regional
Competitive Prediction-Aware Online Algorithms for Energy Generation Scheduling in Microgrids
Online decision-making in the presence of uncertain future information is
abundant in many problem domains. In the critical problem of energy generation
scheduling for microgrids, one needs to decide when to switch energy supply
between a cheaper local generator with startup cost and the costlier on-demand
external grid, considering intermittent renewable generation and fluctuating
demands. Without knowledge of future input, competitive online algorithms are
appealing as they provide optimality guarantees against the optimal offline
solution. In practice, however, future input, e.g., wind generation, is often
predictable within a limited time window, and can be exploited to further
improve the competitiveness of online algorithms. In this paper, we exploit the
structure of information in the prediction window to design a novel
prediction-aware online algorithm for energy generation scheduling in
microgrids. Our algorithm achieves the best competitive ratio to date for this
important problem, which is at most where
is the prediction window size. We also characterize a non-trivial lower
bound of the competitive ratio and show that the competitive ratio of our
algorithm is only away from the lower bound, when a few hours of
prediction is available. Simulation results based on real-world traces
corroborate our theoretical analysis and highlight the advantage of our new
prediction-aware design.Comment: This paper has been accepted into ACM e-Energy 2022 and will appear
in the conference proceeding
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