274 research outputs found
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Network-constrained models of liberalized electricity markets: the devil is in the details
Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions
Network-constrained models of liberalized electricity markets: the devil is in the details
Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions.Market power, Electricity, Networks, Numeric models, Model comparison
Investment and Pricing with Spectrum Uncertainty: A Cognitive Operator's Perspective
This paper studies the optimal investment and pricing decisions of a
cognitive mobile virtual network operator (C-MVNO) under spectrum supply
uncertainty. Compared with a traditional MVNO who often leases spectrum via
long-term contracts, a C-MVNO can acquire spectrum dynamically in short-term by
both sensing the empty "spectrum holes" of licensed bands and dynamically
leasing from the spectrum owner. As a result, a C-MVNO can make flexible
investment and pricing decisions to match the current demands of the secondary
unlicensed users. Compared to dynamic spectrum leasing, spectrum sensing is
typically cheaper, but the obtained useful spectrum amount is random due to
primary licensed users' stochastic traffic. The C-MVNO needs to determine the
optimal amounts of spectrum sensing and leasing by evaluating the trade off
between cost and uncertainty. The C-MVNO also needs to determine the optimal
price to sell the spectrum to the secondary unlicensed users, taking into
account wireless heterogeneity of users such as different maximum transmission
power levels and channel gains. We model and analyze the interactions between
the C-MVNO and secondary unlicensed users as a Stackelberg game. We show
several interesting properties of the network equilibrium, including threshold
structures of the optimal investment and pricing decisions, the independence of
the optimal price on users' wireless characteristics, and guaranteed fair and
predictable QoS among users. We prove that these properties hold for general
SNR regime and general continuous distributions of sensing uncertainty. We show
that spectrum sensing can significantly improve the C-MVNO's expected profit
and users' payoffs.Comment: A shorter version appears in IEEE INFOCOM 2010. This version has been
submitted to IEEE Transactions on Mobile Computin
Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.
Ph.D. Thesis. University of HawaiÊ»i at MÄnoa 2017
An Architecture for Distributed Energies Trading in Byzantine-Based Blockchain
With the development of smart cities, not only are all corners of the city
connected to each other, but also connected from city to city. They form a
large distributed network together, which can facilitate the integration of
distributed energy station (DES) and corresponding smart aggregators.
Nevertheless, because of potential security and privacy protection arisen from
trustless energies trading, how to make such energies trading goes smoothly is
a tricky challenge. In this paper, we propose a blockchain-based multiple
energies trading (B-MET) system for secure and efficient energies trading by
executing a smart contract we design. Because energies trading requires the
blockchain in B-MET system to have high throughput and low latency, we design a
new byzantine-based consensus mechanism (BCM) based on node's credit to improve
efficiency for the consortium blockchain under the B-MET system. Then, we take
combined heat and power (CHP) system as a typical example that provides
distributed energies. We quantify their utilities, and model the interactions
between aggregators and DESs in a smart city by a novel multi-leader
multi-follower Stackelberg game. It is analyzed and solved by reaching Nash
equilibrium between aggregators, which reflects the competition between
aggregators to purchase energies from DESs. In the end, we conduct plenty of
numerical simulations to evaluate and verify our proposed model and algorithms,
which demonstrate their correctness and efficiency completely
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