Optimal Pricing in Electrical Networks over Space and Time

Bohn, Roger E.; Caramanis, Michael C.; Schweppe, Fred C.

doi:10.2307/2555444

Cited by 344 publications

(141 citation statements)

References 12 publications

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“…Our valuation model rests on solving an optimization problem. At any time it minimizes the total costs of electricity generation and delivery; in this sense it draws on Bohn et al [8]. A distinctive feature of our model is that the optimization process is subject to the behavior of the stochastic variables (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…In other words, it does not include any option to delay or otherwise alter the project. 8 It therefore does not address the question of the optimal time to expand. There are other ways to relieve congestion in addition to grid expansions (see for instance Ilić et al [35] or Buijs et al [12]), but they fall beyond the scope of this paper.…”

Section: Methodsmentioning

confidence: 99%

“…We aim to value the positive impacts of investments in network expansion through the 8 Actual practice sometimes adopts this approach. For example, the New England system undertakes market efficiency upgrades (investments designed to reduce bulk system-wide costs) where the net present value of the reduction in system costs exceeds the net present value of the transmission investment; Sakhrani and Parsons [53].…”

Section: The Modelmentioning

confidence: 99%

“…The incentives to co-operate and undertake beneficial investments further depend on the business models adopted in different electricity markets (Wu et al [60]) and on network tariff architectures (Sakhrani and Parsons [53]). 6 There would be no further problem in using our model for a yearly period on an hourly basis (8,760 steps) apart from the increase in the time required for computation.…”

Section: Introductionmentioning

confidence: 99%

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Market-based valuation of transmission network expansion. A heuristic application in GB

Gómez

Abadie

Neufville

et al. 2012

Energy

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Transmission investments are currently needed to meet an increasing electricity demand, to address security of supply concerns, and to reach carbon emissions targets. A key issue when assessing the benefits from an expanded grid concerns the valuation of the uncertain cash flows that result from the expansion. We develop a valuation model which combines optimization techniques, Monte Carlo simulation over the expansion project lifetime, and market data from futures contracts on commodities. The model allows for random failures in generation and transmission infrastructure. Uncertainty stems also from nodal loads, fuel prices, allowance prices, wind generation, and hydro generation. Thus the model accounts for the stochastic dynamics on both the demand side and the supply side. To demonstrate the model by example, we consider a simplified network with two nodes. It is intended to broadly resemble the power generation sectors in England/Wales and Scotland. We then focus on the proposed Western HVDC subsea link. We simulate the whole distribution of effects on system costs, carbon emissions, and unserved load.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: The Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Market-based valuation of transmission network expansion. A heuristic application in GB

Gómez

Abadie

Neufville

et al. 2012

Energy

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“…Compared to direct control methods for congestion management [8,9], market-based methods maximizes the social welfare, cause least discomfort to customers and encourage more participation in the energy planning. By extending LMP [10] from transmission networks to distribution networks, [11,12] have developed the Distribution LMP (DLMP) concept and applied it to handle the congestion issues in distribution networks with DG"s. In DLMP, the local DGs will be properly subsidized if they produce more power and reduce the energy requirement at the local bus from remote areas during congestion periods.…”

Section: Introductionmentioning

confidence: 99%

Efficient Energy Management System for Smart Grid Using Bacterial Foraging Optimization Technique

Raviprabhakaran¹

2017

ijecs

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Distributed devices in smart grid systems are decentralized and connected to the power grid. The connection made through different types of equipment transmits. This produces the numerous energy losses, when power flows from one bus to another bus. The most efficient approaches to reduce energy losses is to integrate the renewable energy resources in Distributed Generation's (DG's). The uncertainty of DG may cause instability issues. The major issue includes congestion in the power grid due to the sudden power consumption by the customers, which affects the efficient energy delivery. Energy management with DG regulation is one of the most efficient solutions to solve these instability issues. In the considered power system with DG's and consumers, the Locational Marginal Pricing (LMP) based unified Energy Management System (uEMS) model is considered. This model increases the profit benefits for DG's and increases the stability of Distributed Energy System (DES). In this paper, the Bacterial Foraging Optimization (BFO) is employed to reduce losses i.e. based on Loss Reduction Allocation (LRA) method. Using LRA method the energy loss reduction is calculated and this model accurately rewards DG contribution and offers a good competitive market. Moreover, the entire DG's profit is increase by the BFO technique. The IEEE 37 bus feeder system is to be considered to validate the proposed uEMS model to increase the DG system stability. Furthermore, this implementation gives the idea of formulating efficient energy management system of future Indian scenario.

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References

2014

The Economics of Electricity Markets

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Optimal Pricing in Electrical Networks over Space and Time

Cited by 344 publications

References 12 publications

Market-based valuation of transmission network expansion. A heuristic application in GB

Market-based valuation of transmission network expansion. A heuristic application in GB

Efficient Energy Management System for Smart Grid Using Bacterial Foraging Optimization Technique

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