Transmission and Wind Power Investment

Baringo, Luis; Conejo, Antonio J.

doi:10.1109/tpwrs.2011.2170441

Cited by 177 publications

(105 citation statements)

References 18 publications

Supporting

Mentioning

103

Contrasting

Unclassified

Order By: Relevance

“…Similar to [20], the yearly load curve is approximated through a piecewise constant duration curve containing several demand blocks and shown in Figure 1. This manifestation can be regarded as rearranging the yearly time sequence load curve into a quantity descending sequence, and merging periods with similar load levels into a single demand block.…”

Section: Load Modelingmentioning

confidence: 99%

“…The carbon emission coefficient e i = 44c i /12, because the molecular weight of carbon and CO 2 is 12 and 44, respectively, this means burning 1kg coal produces 44/12 kg CO 2 . As for the parameters of load blocks, the demand levels are assumed according to the capacity of generators, the time durations are the same as that in [20]. Note that the parameter b is the equivalent production cost of generators, which depends on multiple factors, such as the pure generation cost, CO 2 /SO 2 capture cost, maintenance fee, repayment for the construction investment and so on.…”

Section: -Unit Systemmentioning

confidence: 99%

See 1 more Smart Citation

Taxing Strategies for Carbon Emissions: A Bilevel Optimization Approach

et al. 2014

View full text Add to dashboard Cite

This paper presents a quantitative and computational method to determine the optimal tax rate among generating units. To strike a balance between the reduction of carbon emission and the profit of energy sectors, the proposed bilevel optimization model can be regarded as a Stackelberg game between the government agency and the generation companies. The upper-level, which represents the government agency, aims to limit total carbon emissions within a certain level by setting optimal tax rates among generators according to their emission performances. The lower-level, which represents decision behaviors of the grid operator, tries to minimize the total production cost under the tax rates set by the government. The bilevel optimization model is finally reformulated into a mixed integer linear program (MILP) which can be solved by off-the-shelf MILP solvers. Case studies on a 10-unit system as well as a provincial power grid in China demonstrate the validity of the proposed method and its capability in practical applications.

show abstract

Section: Load Modelingmentioning

confidence: 99%

Section: -Unit Systemmentioning

confidence: 99%

Taxing Strategies for Carbon Emissions: A Bilevel Optimization Approach

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The susceptance of a line, , and, thus, the power flow, , depend on the chosen capacity level. Following [15], we use a DC load-flow approximation for network power flows, which is an acceptable convention in power systems economics as long as voltage angle differences are small, and assume that the realized power flow on line for capacity level in period and scenario is proportional to the susceptance and voltage angle difference, i.e.,…”

Section: A Assumptionsmentioning

confidence: 99%

“…denotes variables of unrestricted sign. Constraint (5) defines the flow on each line for each capacity level as a function of the difference in voltage angles, transmission capacity choice (fixed at the upper level), and line susceptance [15]. The upper and lower limits on transmission flows for each capacity level of each line are set by (6) and (7), respectively, while (8) indicates the realized flow on each line.…”

Section: ) Mi's Lower-level Problemmentioning

confidence: 99%

Transmission and wind investment in a deregulated electricity industry

Maurovich-Horvat

Boomsma

Fleten

et al. 2013

2013 10th International Conference on the European Energy Market (EEM)

View full text Add to dashboard Cite

Abstract-Adoption of dispersed renewable energy technologies requires transmission network expansion. Besides the transmission system operator (TSO), restructuring of electricity industries has introduced a merchant investor (MI), who earns congestion rents from constructing new lines. We compare these two market designs via a stochastic bi-level programming model that has either the MI or the TSO making transmission investment decisions at the upper level and power producers determining generation investment and operation at the lower level while facing wind power variability. We find that social welfare is always higher under the TSO because the MI has incentive to boost congestion rents by restricting capacities of transmission lines. Such strategic behavior also limits investment in wind power by producers. However, regardless of the market design (MI or TSO), when producers behave à la Cournot, a higher proportion of energy is produced by wind. In effect, withholding of generation capacity by producers prompts more transmission investment since the TSO aims to increase welfare by subsidizing wind and the MI creates more flow to maximize profit.Index Terms-Market design, mathematical programming with equilibrium constraints (MPEC), transmission, wind power.

show abstract

“…The model was transformed into a large scale Mixed Integer Linear Programming (MILP) problem and solved using CPLEX. The same method was used in a transmission planning problem considering wind power output [6]. Nick et al [7] established an optimal wind farm allocation model considering the capacity limits of the transmission networks.…”

Section: Introductionmentioning

confidence: 99%

Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit

Wang¹,

Zhang²,

Chen³

et al. 2015

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-Wind power planning aims to locate and size wind farms optimally. Traditionally, wind power planners tend to choose the wind farms with the richest wind resources to maximize the energy benefit. However, the capacity benefit of wind power should also be considered in large-scale clustered wind farm planning because the correlation among the wind farms exerts an obvious influence on the capacity benefit brought about by the combined wind power. This paper proposes a planning model considering both the energy and the capacity benefit of the wind farms. The capacity benefit is evaluated by the wind power capacity credit. The Ordinal Optimization (OO) Theory, capable of handling problems with non-analytical forms, is applied to address the model. To verify the feasibility and advantages of the model, the proposed model is compared with a widely used genetic algorithm (GA) via a modified IEEE RTS-79 system and the real world case of Ningxia, China. The results show that the diversity of the wind farm enhances the capacity credit of wind power.

show abstract

Transmission and Wind Power Investment

Cited by 177 publications

References 18 publications

Taxing Strategies for Carbon Emissions: A Bilevel Optimization Approach

Taxing Strategies for Carbon Emissions: A Bilevel Optimization Approach

Transmission and wind investment in a deregulated electricity industry

Ordinal Optimization Theory Based Planning for Clustered Wind Farms Considering the Capacity Credit

Contact Info

Product

Resources

About