159,969 research outputs found
Fairs for e-commerce: the benefits of aggregating buyers and sellers
In recent years, many new and interesting models of successful online
business have been developed. Many of these are based on the competition
between users, such as online auctions, where the product price is not fixed
and tends to rise. Other models, including group-buying, are based on
cooperation between users, characterized by a dynamic price of the product that
tends to go down. There is not yet a business model in which both sellers and
buyers are grouped in order to negotiate on a specific product or service. The
present study investigates a new extension of the group-buying model, called
fair, which allows aggregation of demand and supply for price optimization, in
a cooperative manner. Additionally, our system also aggregates products and
destinations for shipping optimization. We introduced the following new
relevant input parameters in order to implement a double-side aggregation: (a)
price-quantity curves provided by the seller; (b) waiting time, that is, the
longer buyers wait, the greater discount they get; (c) payment time, which
determines if the buyer pays before, during or after receiving the product; (d)
the distance between the place where products are available and the place of
shipment, provided in advance by the buyer or dynamically suggested by the
system. To analyze the proposed model we implemented a system prototype and a
simulator that allow to study effects of changing some input parameters. We
analyzed the dynamic price model in fairs having one single seller and a
combination of selected sellers. The results are very encouraging and motivate
further investigation on this topic
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy
management mechanism for the smart grid that enables each prosumer of the
network to participate in energy trading with one another and the grid. This
poses a significant challenge in terms of modeling the decision-making process
of each participant with conflicting interest and motivating prosumers to
participate in energy trading and to cooperate, if necessary, for achieving
different energy management goals. Therefore, such decision-making process
needs to be built on solid mathematical and signal processing tools that can
ensure an efficient operation of the smart grid. This paper provides an
overview of the use of game theoretic approaches for P2P energy trading as a
feasible and effective means of energy management. As such, we discuss various
games and auction theoretic approaches by following a systematic classification
to provide information on the importance of game theory for smart energy
research. Then, the paper focuses on the P2P energy trading describing its key
features and giving an introduction to an existing P2P testbed. Further, the
paper zooms into the detail of some specific game and auction theoretic models
that have recently been used in P2P energy trading and discusses some important
finding of these schemes.Comment: 38 pages, single column, double spac
Cooperatives for demand side management
We propose a new scheme for efficient demand side management for the Smart Grid. Specifically, we envisage and promote the formation of cooperatives of medium-large consumers and equip them (via our proposed mechanisms) with the capability of regularly participating in the existing electricity markets by providing electricity demand reduction services to the Grid. Based on mechanism design principles, we develop a model for such cooperatives by designing methods for estimating suitable reduction amounts, placing bids in the market and redistributing the obtained revenue amongst the member agents. Our mechanism is such that the member agents have no incentive to show artificial reductions with the aim of increasing their revenue
A Distributed Demand-Side Management Framework for the Smart Grid
This paper proposes a fully distributed Demand-Side Management system for
Smart Grid infrastructures, especially tailored to reduce the peak demand of
residential users. In particular, we use a dynamic pricing strategy, where
energy tariffs are function of the overall power demand of customers. We
consider two practical cases: (1) a fully distributed approach, where each
appliance decides autonomously its own scheduling, and (2) a hybrid approach,
where each user must schedule all his appliances. We analyze numerically these
two approaches, showing that they are characterized practically by the same
performance level in all the considered grid scenarios. We model the proposed
system using a non-cooperative game theoretical approach, and demonstrate that
our game is a generalized ordinal potential one under general conditions.
Furthermore, we propose a simple yet effective best response strategy that is
proved to converge in a few steps to a pure Nash Equilibrium, thus
demonstrating the robustness of the power scheduling plan obtained without any
central coordination of the operator or the customers. Numerical results,
obtained using real load profiles and appliance models, show that the
system-wide peak absorption achieved in a completely distributed fashion can be
reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to
meet the growing energy demand
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