Transmission network planning under a pricebased demand response program

Kazerooni, Ali; Mutale, Joseph

doi:10.1109/tdc.2010.5484267

Cited by 10 publications

(12 citation statements)

References 13 publications

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“…The demand response (DR) cost equals the incentive cost which is given to the customers for each kWh. For calculating the hourly DR costs in peak hours from 9:00 p.m. to 12:00 a.m., the average DR cost for this period is calculated as follows: By using Equation (27), the hourly DR cost is 670.96 ($/h). According to Table 3 After performing the demand response program, the TEP was performed.…”

Section: Study Of the Network And Simulation Resultsmentioning

confidence: 99%

“…By using Equation (27), the hourly DR cost is 670.96 ($/h). According to Table 3, in scenario 3 in which the optimized objective function does not contain line construction costs, there is no limitation on the number of added lines.…”

Section: Cost Of Line ($/H) Total Cost ($/H)mentioning

confidence: 99%

“…Similar to Table 3, the results of TEP for scenarios 3 and 4 are shown in Table 4. By using Equation (27), the hourly DR cost is 900 ($/h). Based on the tabulation, we observed that in scenario 4, including all three costs of generation, losses, and line construction, the total cost is lower than in scenario 3, in which the objective function excludes the line construction cost.…”

Section: Cost Of Line ($/H) Total Cost ($/H)mentioning

confidence: 99%

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Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources

Zakeri

Abyaneh

2017

Energies

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Transmission Expansion Planning (TEP) involves determining if and how transmission lines should be added to the power grid so that the operational and investment costs are minimized. TEP is a major issue in smart grid development, where demand response resources affect short-and long-term power system decisions, and these in turn, affect TEP. First, this paper discusses the effects of demand response programs on reducing the final costs of a system in TEP. Then, the TEP problem is solved using a Teaching Learning Based Optimization (TLBO) algorithm taking into consideration power generation costs, power loss, and line construction costs. Simulation results show the optimal effect of demand response programs on postponing the additional cost of investments for supplying peak load.

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Section: Study Of the Network And Simulation Resultsmentioning

confidence: 99%

Section: Cost Of Line ($/H) Total Cost ($/H)mentioning

confidence: 99%

Section: Cost Of Line ($/H) Total Cost ($/H)mentioning

confidence: 99%

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Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources

Zakeri

Abyaneh

2017

Energies

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“…According to the electric power research institute (EPRI), it is expected that by 2020 up to 35% of the total vehicles in the U.S. will be PEVs [32]. The PEVs either in the form of source as a vehicle to grid (V2G) technology or load as a grid to vehicle (G2V) technology studies in the different fields of the power systems have been reported in the literature recently [33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning

Rathore

Roy

2016

International Journal of Electrical Power & Energy Systems

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“…But either the reinforcement of the network or the FACTS are time-consuming and expensive. In some references [4][5], the nodal price in congestion area is raised, which can curtail the electricity demand with higher elasticity and attract investors to build new thermal generation in the electricity shortage area. But this will influence the economy and reliability of the system, and the new-built thermal generation still lags behind the electricity demand.…”

Section: Introductionmentioning

confidence: 99%

Economic benefits evaluating of wind power integration considering transmission congestion

Bie

Zhang

Deng

et al. 2016

2016 Clemson University Power Systems Conference (PSC)

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In this paper, a model of evaluating the economic benefits brought by wind power integration considering transmission congestion is proposed, in which the Probabilistic Load Flow based on Cumulants Method (PLF-CM) is applied to calculate the probability distribution of section power flow. According to the case study, the transmission congestion is the main factor that limits the utilization of wind power, which also limits the economic benefits of wind power. Moreover, the different penetration of wind power and different integration places of wind farms also bring different benefits to the grid, which can provide some references of choosing appropriate wind power integrating places and deciding the best integrating wind power capacity for power grid plan-makers.

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Transmission network planning under a pricebased demand response program

Cited by 10 publications

References 13 publications

Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources

Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources

Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning

Economic benefits evaluating of wind power integration considering transmission congestion

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