Optimum day-ahead clearing of energy and reserve markets with wind power generation using anticipated real-time adjustment costs

Reddy, S. Surender; Bijwe, P.R.; Abhyankar, A. R.

doi:10.1016/j.ijepes.2015.03.002

Cited by 52 publications

(24 citation statements)

References 35 publications

(41 reference statements)

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“…This modeling is about the optimal scheduling of wind thermal–pumped storage plant. The total cost of the coordinated system, being a major objective function, is as follows:

\begin{matrix} lefttrue \\ minimize \\ {italicCOST}_{total} = {italicCOST}_{fuel} + {italicCOST}_{direct} + {italicCOST}_{UE} + {italicCOST}_{OE} + {italicCOST}_{reserve} + {italicCOST}_{realtime}, \end{matrix}

where COST total is the total cost of the system. The other cost terms are explained as follows.…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

“…The first term of the equation is the fuel cost of the conventional power plant expressed as,

{COST}_{fuel} = \sum_{i = 1}^{NG} ((), α_{i} {PG}_{i}^{2} + β_{i} {PG}_{i} + γ_{i}),

where, α , β , γ are the cost coefficient. PG i indicates the thermal power produced in i th unit, and NG indicates the number of thermal units.…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

“…The second term in the cost equation is the direct cost paid to the wind power providers. This cost equation can be expressed as the function of scheduled wind power,

{COST}_{direct} = \sum_{j = 1}^{NW} d_{w} {PW}_{italicsh, j},

where, d w is the direct purchase cost taken by the wind farms and PW sh , j is the wind power of the j th farm that is scheduled. The number of wind power farm is indicated by NW .…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

“…The next two terms of the objective function depend on the wind power availability. The COST UE can be described as,

{COST}_{UE} = \sum_{j = 1}^{NW} λ_{u} ((), {PW}_{italicav, j} - {PW}_{italicsh, j}),

where, λ u is the penalty function for excess wind power production.…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

See 3 more Smart Citations

A cost‐optimized power management strategy for combined wind thermal–pumped hydro generation considering wind power uncertainty

Bhattacharya

Chakraborty

Mandal

2019

Int Trans Electr Energ Syst

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Summary The trend of today's power system is to incorporate more green power in the conventional grid to mitigate the power demand and to minimize environmental pollution. This paper proposes a multi‐energy system with wind thermal–pump storage plant aiming to minimize the total cost of operation. In this paper, a forecasted data of wind speed is considered. Because of the variable nature of wind power, real‐time generation scheduling may lead to deviation of forecasted thermal power generation. This paper works on a power management strategy (PMS) where the gap between the day‐ahead scheduled thermal power and the real‐time scheduled power is minimized optimizing the operation cost. Cultural algorithm (CA) is used here very efficiently in a modified UI 62 bus. It is observed from the simulation results that the proposed strategy can effectively reduce the power deviation fluctuations with reducing the operating as well as runtime cost. A comparison of total cost between CA and particle swarm optimization (PSO) is drawn in this paper to observe the efficacy of the proposed method.

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“…This modeling is about the optimal scheduling of wind thermal–pumped storage plant. The total cost of the coordinated system, being a major objective function, is as follows:

\begin{matrix} lefttrue \\ minimize \\ {italicCOST}_{total} = {italicCOST}_{fuel} + {italicCOST}_{direct} + {italicCOST}_{UE} + {italicCOST}_{OE} + {italicCOST}_{reserve} + {italicCOST}_{realtime}, \end{matrix}

where COST total is the total cost of the system. The other cost terms are explained as follows.…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

“…The first term of the equation is the fuel cost of the conventional power plant expressed as,

{COST}_{fuel} = \sum_{i = 1}^{NG} ((), α_{i} {PG}_{i}^{2} + β_{i} {PG}_{i} + γ_{i}),

where, α , β , γ are the cost coefficient. PG i indicates the thermal power produced in i th unit, and NG indicates the number of thermal units.…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

“…The second term in the cost equation is the direct cost paid to the wind power providers. This cost equation can be expressed as the function of scheduled wind power,

{COST}_{direct} = \sum_{j = 1}^{NW} d_{w} {PW}_{italicsh, j},

where, d w is the direct purchase cost taken by the wind farms and PW sh , j is the wind power of the j th farm that is scheduled. The number of wind power farm is indicated by NW .…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

“…The next two terms of the objective function depend on the wind power availability. The COST UE can be described as,

{COST}_{UE} = \sum_{j = 1}^{NW} λ_{u} ((), {PW}_{italicav, j} - {PW}_{italicsh, j}),

where, λ u is the penalty function for excess wind power production.…”

Section: Formulation Of Proposed Modelmentioning

confidence: 99%

See 2 more Smart Citations

A cost‐optimized power management strategy for combined wind thermal–pumped hydro generation considering wind power uncertainty

Bhattacharya

Chakraborty

Mandal

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Research [21] further links up the day-ahead scheduling and real-time scheduling by a more meticulous generation dispatch mode to formulate a comprehensive strategy. Researches [22][23][24][25][26] introduce the market mechanism to ensure the economic efficiency of power system considering the risk due to uncertainties. The overestimation and underestimation costs of the renewable energy sources are considered in the objective, which is similar to the upregulation and downregulation in the adjustment stage.…”

Section: Introductionmentioning

confidence: 99%

The Integration of Wind-Solar-Hydropower Generation in Enabling Economic Robust Dispatch

Peng

Liu

2019

Mathematical Problems in Engineering

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Currently the renewable energies including wind power and photovoltaic power have been increasingly deployed in power system to achieve contamination free and environmental-friendly power production. However, due to the natural characteristics of wind and solar, both wind power and photovoltaic power contain uncertainty and randomness which may significantly impact the stability, security, and economic efficiency of the conventional power system mainly consisted by hydropower and thermal power. To deal with the issue, this paper presents a two-stage robust model which is able to achieve the optimal day-ahead dispatch strategy in the worst-case scenario of wind and photovoltaic outputs. Because of the strong interactions between the two stages, the original optimization has been decomposed into the day-ahead dispatch master problem and the additional adjustment subproblem considering the uncertainty and randomness of the wind and the photovoltaic outputs. Also, the piecewise linearization technique is employed to convert the original problem into a MILP problem. Afterward, the dualization of the additional adjustment subproblem can be obtained by using linear programming strong duality theory. Additionally, the Big-M method enables the linearization of the dual model. The interacted-iterations between the master problem and the subproblem are successfully implemented which can ultimately figure out the optimal day-ahead dispatch strategy of the power system with conventional and renewable energies.

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Optimum day-ahead clearing of energy and reserve markets with wind power generation using anticipated real-time adjustment costs

Cited by 52 publications

References 35 publications

A cost‐optimized power management strategy for combined wind thermal–pumped hydro generation considering wind power uncertainty

A cost‐optimized power management strategy for combined wind thermal–pumped hydro generation considering wind power uncertainty

The Integration of Wind-Solar-Hydropower Generation in Enabling Economic Robust Dispatch

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