9 resultados para Congestion pricing

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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This paper considers the congestion effects on emission and consumers' allocated cost. In order to consider some environmental and operational effects of congestion, an environmental constrained active-reactive optimal power flow (AROPF) considering capability curve is presented. On outage conditions, the total cost of the system will increase. On the other hand in power systems, the operating cost and system emission have conflicted objectives, then it may be concluded that the outage in the system may lead to a total emission decrease. In this paper the famous Aumann-Shapley method is used as a pricing methodology. Two case studies such as 14-bus and US-bus IEEE test systems are conducted. Results demonstrate that, although the line outage in power systems leads to increase the total cost, the amount of emission depending on the place where the outage occurs can be more than, less than or equal to the normal conditions' emission. Also results show that although from power sellers' standpoint the well-known Aumann-Shapley method is a precise pricing method to cover the incurred cost with an acceptable error that can show the real effect of congestion on consumers' cost, from consumers' standpoint it is not a good method for cost allocation, because some consumers will face with an increase in cost and the others will face with a decrease on their cost.

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There is a well-developed framework, the Black-Scholes theory, for the pricing of contracts based on the future prices of certain assets, called options. This theory assumes that the probability distribution of the returns of the underlying asset is a Gaussian distribution. However, it is observed in the market that this hypothesis is flawed, leading to the introduction of a fudge factor, the so-called volatility smile. Therefore, it would be interesting to explore extensions of the Black-Scholes theory to non-Gaussian distributions. In this paper, we provide an explicit formula for the price of an option when the distributions of the returns of the underlying asset is parametrized by an Edgeworth expansion, which allows for the introduction of higher independent moments of the probability distribution, namely skewness and kurtosis. We test our formula with options in the Brazilian and American markets, showing that the volatility smile can be reduced. We also check whether our approach leads to more efficient hedging strategies of these instruments. (C) 2004 Elsevier B.V. All rights reserved.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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A bilevel programming approach for the optimal contract pricing of distributed generation (DG) in distribution networks is presented. The outer optimization problem corresponds to the owner of the DG who must decide the contract price that would maximize his profits. The inner optimization problem corresponds to the distribution company (DisCo), which procures the minimization of the payments incurred in attending the expected demand while satisfying network constraints. The meet the expected demand the DisCo can purchase energy either form the transmission network through the substations or form the DG units within its network. The inner optimization problem is substituted by its Karush- Kuhn-Tucker optimality conditions, turning the bilevel programming problem into an equivalent single-level nonlinear programming problem which is solved using commercially available software. © 2010 IEEE.

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In this paper, a novel methodology to price the reactive power support ancillary service of Distributed Generators (DGs) with primary energy source uncertainty is shown. The proposed methodology provides the service pricing based on the Loss of Opportunity Costs (LOC) calculation. An algorithm is proposed to reduce the uncertainty present in these generators using Multiobjective Power Flows (MOPFs) implemented in multiple probabilistic scenarios through Monte Carlo Simulations (MCS), and modeling the time series associated with the generation of active power from DGs through Markov Chains (MC). © 2011 IEEE.

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Distributed Generation, microgrid technologies, two-way communication systems, and demand response programs are issues that are being studied in recent years within the concept of smart grids. At some level of enough penetration, the Distributed Generators (DGs) can provide benefits for sub-transmission and transmission systems through the so-called ancillary services. This work is focused on the ancillary service of reactive power support provided by DGs, specifically Wind Turbine Generators (WTGs), with high level of impact on transmission systems. The main objective of this work is to propose an optimization methodology to price this service by determining the costs in which a DG incurs when it loses sales opportunity of active power, i.e, by determining the Loss of Opportunity Costs (LOC). LOC occur when more reactive power is required than available, and the active power generation has to be reduced in order to increase the reactive power capacity. In the optimization process, three objectives are considered: active power generation costs of DGs, voltage stability margin of the system, and losses in the lines of the network. Uncertainties of WTGs are reduced solving multi-objective optimal power flows in multiple probabilistic scenarios constructed by Monte Carlo simulations, and modeling the time series associated with the active power generation of each WTG via Fuzzy Logic and Markov Chains. The proposed methodology was tested using the IEEE 14 bus test system with two WTGs installed. © 2011 IEEE.

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In this study, a novel approach for the optimal location and contract pricing of distributed generation (DG) is presented. Such an approach is designed for a market environment in which the distribution company (DisCo) can buy energy either from the wholesale energy market or from the DG units within its network. The location and contract pricing of DG is determined by the interaction between the DisCo and the owner of the distributed generators. The DisCo intends to minimise the payments incurred in meeting the expected demand, whereas the owner of the DG intends to maximise the profits obtained from the energy sold to the DisCo. This two-agent relationship is modelled in a bilevel scheme. The upper-level optimisation is for determining the allocation and contract prices of the DG units, whereas the lower-level optimisation is for modelling the reaction of the DisCo. The bilevel programming problem is turned into an equivalent single-level mixed-integer linear optimisation problem using duality properties, which is then solved using commercially available software. Results show the robustness and efficiency of the proposed model compared with other existing models. As regards to contract pricing, the proposed approach allowed to find better solutions than those reported in previous works. © The Institution of Engineering and Technology 2013.

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The effects splenic dilatation induced by acepromazine in a prospective, randomized study. Thirtythree adult mongrel dogs were divided into two groups designated as AG (acepromazine 0.05 mg/kg, i.v., n = 23) and CG (0.9% sodium chloride administered at a similar volume, n = 10). In both groups underwent sonographic examinations before (T0) and fifteen minutes (T15) after drug injection. The thickness spleen and splenic vein width were measured. Higher thickness was found in the AG group at T15 (2.47 cm) when compared to that at T0 (2.06 cm, p = 0.016), while the T0 (2.33 cm) and T15 (2.39 cm) measures did not differ within the CG group. Moreover, the splenic vein width was higher (p = 0.013) at T15 than at T0 in the AG group. Based on results of this study, we concluded that acepromazine, in doses of 0.05 mg/kg, promotes splenomegaly in dogs after fifteen minutes of the injection.