A Convex Micro-Grid-Based Optimization Model for Planning of Resilient and Sustainable Distribution Systems Considering Feeders Routing and Siting/Sizing of Substations and DG Units

Behzadi, Saeed; Bagheri, Amir

doi:10.1016/j.scs.2023.104787

Cited by 14 publications

(2 citation statements)

References 24 publications

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“…In the study, uncertainties about PV generation and load demand have been ignored. In [10], authors have suggested a framework to optimally solve DNP problem, considering the siting and sizing of substations and distributed generation (DG) units. For this aim, a convex model has been introduced and four system costs (investment, operation, emission, and resiliency) have been minimized.…”

Section: Introductionmentioning

confidence: 99%

A risk‐averse strategy based on information gap decision theory for optimal placement of service transformers in distribution networks

Alipour,

Askarzadeh

2024

IET Generation Trans & Dist

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In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO.

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Section: Introductionmentioning

confidence: 99%

A risk‐averse strategy based on information gap decision theory for optimal placement of service transformers in distribution networks

Alipour,

Askarzadeh

2024

IET Generation Trans & Dist

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“…Results demonstrate the enhancement of system performance and the reduction of investment costs. In [17], authors have proposed optimal microgrid formation for resilient planning. The formulation optimizes conventional and renewable-based DG siting and sizing, feeder routing and type, as well as high voltage/MV substation sizing and placement to create micro-grids that are resilient against extreme events.…”

Section: Introductionmentioning

confidence: 99%

An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers

Alipour,

Askarzadeh

2023

IET Renewable Power Gen

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Among the distribution network planning problems, allocation of transformers is one of the most important and challenging ones. On the other hand, owing to the widespread growth of distributed generations (DGs), their inclusion in the planning problems is vital. This paper proposes an efficient optimization framework for simultaneous allocation of photovoltaic (PV) systems and service transformers in the distribution network. For this aim, to improve the network performance, location and size of the transformers and PV units are optimally determined. Due to the difficulty of the planning problem, four variants of crow search algorithm (CSA) including original CSA, differential CSA (CSAd), directed differential CSA (CSAdd) and chaotic directed differential CSA (CSAcdd) are introduced and applied to the planning problem. To evaluate the impact of PV cost on the results, the planning problem is solved considering different PV system costs. Simulation results show that at the price of $1.25/W, installation of PV systems is cost‐effective, so that over the case study, 400 kW PV system was installed. By decreasing the PV price to $1.2/W, the maximum capacity considered for a PV system is installed. Moreover, on average, CSAd produces more accurate and robust results than the other studied algorithms.

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Optimizing Distribution Systems with Renewable Energy Integration: Hybrid Mud Ring Algorithm‐Quantum Neural Network Approach

2024

Energy Tech

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A hybrid approach is proposed for optimizing distribution systems (DSs) by integrating clean energy sources, specifically photovoltaic (PV) and wind power (WT). The proposed technique combines the mud ring algorithm (MRA) and quantum neural network (QNN), referred to as the MRA‐QNN technique. The primary objective is to minimize power loss and enhance voltage stability. The MRA method generates the control signal of the converter, while the QNN method predicts the control signal based on the MRA output. The effectiveness of the approach is revealed through simulations on standard IEEE 33 bus and 69 bus systems. Implementation in MATLAB shows superior performance compared to existing methods, with lower power loss values. There has been a sustained rise in the system voltage profile (In the WT and PV situations, 0.950. and 93 p.u), as well as a considerable reduction in the active power (AP) losses (to 132.39 kW with PV and 81.23 kW with WT from 362.86 kW). With PV, the entire yearly economic loss is lowered from $158932.68 to just $57996.939, and with WT, it is decreased to $56805.479. With three PVs, the yearly economic loss and active power losses are decreased to 30419.871 $ and 69.449, and 4.27 kW and 1875.930 $, respectively.

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A Convex Micro-Grid-Based Optimization Model for Planning of Resilient and Sustainable Distribution Systems Considering Feeders Routing and Siting/Sizing of Substations and DG Units

Cited by 14 publications

References 24 publications

A risk‐averse strategy based on information gap decision theory for optimal placement of service transformers in distribution networks

A risk‐averse strategy based on information gap decision theory for optimal placement of service transformers in distribution networks

An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers

Optimizing Distribution Systems with Renewable Energy Integration: Hybrid Mud Ring Algorithm‐Quantum Neural Network Approach

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