Transmission Network Planning Under Security and Environmental Constraints

Kazerooni, Ali; Mutale, Joseph

doi:10.1109/tpwrs.2009.2036800

Cited by 74 publications

(45 citation statements)

References 20 publications

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“…But this is typically undertaken using some pre--determined allowance price (assumed to be correct on average at best). Kazerooni and Mutale [39], instead, approximate carbon allowance price by means of a Weibull distribution based on past data. They then introduce the carbon constraint as a changing operation cost in the objective function of transmission network planning.…”

Section: Literature Reviewmentioning

confidence: 99%

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: Literature Reviewmentioning

confidence: 99%

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

Gómez

Abadie

Neufville

et al. 2012

Energy

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“…The power-generation industry, which is the main sector of energy consumption and an important source of CO 2 emissions, will therefore operate the following low-carbon policy [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Substation planning method in an active distribution network under low-carbon economy

Wang

et al. 2015

J. Mod. Power Syst. Clean Energy

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A substation planning method that accounts for the widespread introduction of distributed generators (DGs) in a low-carbon economy is proposed. With the proliferation of DGs, the capacity that DGs contribute to the distribution network has become increasingly important. The capacity of a DG is expressed as a capacity credit (CC) that can be evaluated according to the principle that the reliability index is unchanged before and after the introduction of the DG. A method that employs a weighted Voronoi diagram is proposed for substation planning considering CC. A low-carbon evaluation objective function is added to the substation planning model to evaluate the contribution of DGs to a low-carbon economy. A case study is analyzed to demonstrate the practicality of the proposed method.

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“…Environmental aspects are included in TEP models in [21][22][23]. In [21] and [22], the environmental variable (CO 2 emissions) is included in the objective function.…”

mentioning

confidence: 99%

“…Environmental aspects are included in TEP models in [21][22][23]. In [21] and [22], the environmental variable (CO 2 emissions) is included in the objective function. The TEP problem is solved using genetic and simulated annealing algorithms in [21], while, in [22], the TEP is solved using mixed integer programming and accounting for security constraints and uncertainty in CO 2 prices.…”

mentioning

confidence: 99%

“…In [21] and [22], the environmental variable (CO 2 emissions) is included in the objective function. The TEP problem is solved using genetic and simulated annealing algorithms in [21], while, in [22], the TEP is solved using mixed integer programming and accounting for security constraints and uncertainty in CO 2 prices. On the other hand, in [23], the authors develop a method for internalizing environmental costs in the social cost-benefit analysis of transmission expansions.…”

mentioning

confidence: 99%

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