31,846 research outputs found
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
From Packet to Power Switching: Digital Direct Load Scheduling
At present, the power grid has tight control over its dispatchable generation
capacity but a very coarse control on the demand. Energy consumers are shielded
from making price-aware decisions, which degrades the efficiency of the market.
This state of affairs tends to favor fossil fuel generation over renewable
sources. Because of the technological difficulties of storing electric energy,
the quest for mechanisms that would make the demand for electricity
controllable on a day-to-day basis is gaining prominence. The goal of this
paper is to provide one such mechanisms, which we call Digital Direct Load
Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle
individual requests for energy and digitize them so that they can be
automatically scheduled in a cellular architecture. Specifically, rather than
storing energy or interrupting the job of appliances, we choose to hold
requests for energy in queues and optimize the service time of individual
appliances belonging to a broad class which we refer to as "deferrable loads".
The function of each neighborhood scheduler is to optimize the time at which
these appliances start to function. This process is intended to shape the
aggregate load profile of the neighborhood so as to optimize an objective
function which incorporates the spot price of energy, and also allows
distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications
(JSAC): Smart Grid Communications series, to appea
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
An Artificial Intelligence Framework for Bidding Optimization with Uncertainty inMultiple Frequency Reserve Markets
The global ambitions of a carbon-neutral society necessitate a stable and
robust smart grid that capitalises on frequency reserves of renewable energy.
Frequency reserves are resources that adjust power production or consumption in
real time to react to a power grid frequency deviation. Revenue generation
motivates the availability of these resources for managing such deviations.
However, limited research has been conducted on data-driven decisions and
optimal bidding strategies for trading such capacities in multiple frequency
reserves markets. We address this limitation by making the following research
contributions. Firstly, a generalised model is designed based on an extensive
study of critical characteristics of global frequency reserves markets.
Secondly, three bidding strategies are proposed, based on this market model, to
capitalise on price peaks in multi-stage markets. Two strategies are proposed
for non-reschedulable loads, in which case the bidding strategy aims to select
the market with the highest anticipated price, and the third bidding strategy
focuses on rescheduling loads to hours on which highest reserve market prices
are anticipated. The third research contribution is an Artificial Intelligence
(AI) based bidding optimization framework that implements these three
strategies, with novel uncertainty metrics that supplement data-driven price
prediction. Finally, the framework is evaluated empirically using a case study
of multiple frequency reserves markets in Finland. The results from this
evaluation confirm the effectiveness of the proposed bidding strategies and the
AI-based bidding optimization framework in terms of cumulative revenue
generation, leading to an increased availability of frequency reserves
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