816 research outputs found
Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind
The exceptional benefits of wind power as an environmentally responsible
renewable energy resource have led to an increasing penetration of wind energy
in today's power systems. This trend has started to reshape the paradigms of
power system operations, as dealing with uncertainty caused by the highly
intermittent and uncertain wind power becomes a significant issue. Motivated by
this, we present a new framework using adaptive robust optimization for the
economic dispatch of power systems with high level of wind penetration. In
particular, we propose an adaptive robust optimization model for multi-period
economic dispatch, and introduce the concept of dynamic uncertainty sets and
methods to construct such sets to model temporal and spatial correlations of
uncertainty. We also develop a simulation platform which combines the proposed
robust economic dispatch model with statistical prediction tools in a rolling
horizon framework. We have conducted extensive computational experiments on
this platform using real wind data. The results are promising and demonstrate
the benefits of our approach in terms of cost and reliability over existing
robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System
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 They are no longer applicable to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
Addressing flexibility in energy system models
The present report summarises the discussions and conclusions of the international workshop on "Addressing flexibility in energy system models" held on December 4 and 5 2014 at the premises of the JRC Institute for Energy and Transport in Petten. Around 40 energy modelling experts and researchers from universities, research centres, the power industry, international organisations, and the European Commission (DGs ENER and JRC) met to present and discuss their views on the modelling of flexibility issues, the linkage of energy system models and sector-detailed energy models, the integration of high shares of variable renewable energy sources, and the representation of flexibility needs in power system models. The discussions took into account modelling and data-related methodological aspects, with their limitations and uncertainties, as well as possible alternatives to be implemented within energy system models.JRC.F.6-Energy Technology Policy Outloo
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