Optimal generation scheduling in microgrids using mixed-integer second-order cone programming

Electrification of transportation is one of the new ways to deal with environmental issues. In order to reach sustainable development, efficient integration of the plug‐in electric vehicles (PEVs) into the distribution network (DN) plays a significant role. Establishing PEV intelligent parking lots (IPLs) is one of the important solutions to develop charging stations. However, one of the problems with IPLs is restrictions created by municipalities and DNs while the common parking lots are not faced with these constraints. The aim of this paper is to optimally allocate IPLs in order to minimize system costs through a two‐stage mathematical model. The system cost involves installation cost, cost of exchanging the electrical power with an upstream grid, cost of the electrical power loss, network reliability cost, and emissions cost. In the first stage, the behaviour of IPLs is optimized taking into account the market interactions for enhancing the profit of the IPL owner. In the second stage, the optimal IPL allocation is performed taking various network constraints into consideration. The robust optimization is developed to hedge the DN operator and the IPL owner against risk imposed uncertain variables. The proposed model is a Robust Mix Integer Linear Problem, which is solved using the CPLEX solver in GAMS software. The effectiveness and the efficiency of the proposed model are evaluated on the IEEE 12‐bus test network.

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“…The cost of exchanging the electrical power with the upstream grid is formulated by () as follows [26]:

C o s t^{Grid} (t) = π^{E} (t) \times P^{Grid} (t) \forall t \in T

…”

Section: Problem Formulationmentioning

confidence: 99%

“…where EENS is written as follows [22]: ðh; iÞ is an extra electrical power discharging during the contingencies based on the contract between DNO and PEV's owner and it is denoted as [26]:…”

Section: Objective Functionmentioning

confidence: 99%

Optimal allocation of intelligent parking lots in distribution system: A robust two‐stage optimization model

Mojarad

Sedighizadeh

Dosaranian‐Moghadam

2022

IET Electrical Syst in Trans

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“…The cost of exchanging electrical power with the upstream grid is formulated by (22) as follows 26 :

{Cost}^{Grid} ((), t) = π^{E} ((), t) \times P^{Grid} ((), t) \forall t \in T .

Installation cost of IPLs consists of two terms including fixed costs and variable costs as (23). The fixed costs include municipal and construction license payment to install the IPL.…”

Section: Problem Formulationmentioning

confidence: 99%

A two‐stage stochastic model based on information gap decision theory method for optimal allocation of intelligent parking lots in distribution systems considering severe uncertainties

Mojarad

Sedighizadeh

Dosaranian‐Moghadam

2021

Int Trans Electr Energ Syst

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Summary Electrification of transportation is one of the new ways to deal with environmental issues. In order to reach sustainable development, efficient integration of the plug‐in electric vehicles (PEVs) into the distribution network (DN) plays a significant role. Establishment of PEV intelligent parking lots (IPLs) is one of the important solutions to develop charging stations. However, one of the problems with IPLs is restrictions created by municipalities and DNs while the common parking lots are not faced with these constraints. The aim of this paper is the optimal allocation of IPLs through a two‐stage mathematical model in order to minimize system costs. The system cost involves installation cost, cost of exchanging electrical power with the upstream grid, cost of the electrical power loss, network reliability cost, and emissions cost. In the first stage, the behavior of IPLs is optimized taking into account the market interactions for enhancing the IPL owner's profit. In the second stage, optimal allocation of the IPL is performed considering various network constraints. Demand response is considered as Time of Use program. Information gap decision theory is developed to hedge the DN operator and IPL owner against the risk imposed by the information gap between the forecasted and actual uncertain variables. The effectiveness and efficiency of the proposed model are evaluated on the IEEE 33‐bus test network. In the end, the presented model with simultaneously optimization can lessen the total cost by 7.74%, 15.48%, and 19.27% compared to cases, which only optimize active power loss, reliability, and emissions, respectively.

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“…• CASE A: Minimisation of active power generation cost alone (using HTSW scheduling) [32][33][34] • CASE B: Maximisation of ATC along with minimisation of active power generation cost with renewable energy sources (multi-objective).…”

Section: Problem Formulation Of Atc Along With Htsw Schedulingmentioning

confidence: 99%

Implementation of multi‐objective chaotic mayfly optimisation for hydro‐thermal ‐ solar‐wind scheduling based on available transfer capability problem

Majumdar

Roy

Banerjee

2021

Int Trans Electr Energ Syst

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The electrical power generation from conventional thermal power plants needs to be interconnected with natural resources like solar, wind, hydro units with all-day planning, and operation strategies to save mother nature and meet the current electricity demand. The complexity and size of the power network are increasing rapidly day by day. The enhanced power transfer from one section to another section in the existing grid system is the subject of available transfer capability (ATC), which is the modern power system's critical factor.

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Optimal generation scheduling in microgrids using mixed-integer second-order cone programming

Cited by 14 publications

References 44 publications

Optimal allocation of intelligent parking lots in distribution system: A robust two‐stage optimization model

Optimal allocation of intelligent parking lots in distribution system: A robust two‐stage optimization model

A two‐stage stochastic model based on information gap decision theory method for optimal allocation of intelligent parking lots in distribution systems considering severe uncertainties

Implementation of multi‐objective chaotic mayfly optimisation for hydro‐thermal ‐ solar‐wind scheduling based on available transfer capability problem

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