Simultaneous optimization of re-phasing, reconfiguration and DG placement in distribution networks using BF-SD algorithm

Kaveh, Mohammad Reza; Hooshmand, Rahmat‐Allah; Madani, Seyed M.

doi:10.1016/j.asoc.2017.09.041

Cited by 45 publications

(37 citation statements)

References 25 publications

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“…Figure 5a shows the calculated amounts of W , Switches with lower amounts of distance index (e.g., #1, #18) or with lower amounts of age index (e.g., #8, #33) result in lower values of SI in the short-term operational planning of RCSs. According to Equation (13), daily maximum allowable switching actions for each RCSs can be calculated, as shown in Figure 5b The measured 24-h load curve of the system and the real hourly wholesale electricity market prices are shown in Figure 6 [32]. Due to the capital investment and maintenance cost of switches, the switching cost is considered to be one dollar per action [36].…”

Section: Tests and Resultsmentioning

confidence: 99%

“…The concept behind the proposed SI is to avoid risky and aged RCSs from frequent switching actions The maximum allowable switching actions for RCS k per day can be calculated by the floor function in (13): N SW k,max = SI k N max (13) where, N max is the possible switching actions per day based on the expected lifetime of RCSs determined by the manufacturer and N SW k,max is an integer value in the range [0, N max ]. Accordingly, the following constraint should be satisfied:…”

Section: Hourly Reconfiguration Of Distribution Systemmentioning

confidence: 99%

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A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

et al. 2018

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Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

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Section: Tests and Resultsmentioning

confidence: 99%

Section: Hourly Reconfiguration Of Distribution Systemmentioning

confidence: 99%

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

et al. 2018

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“…Objective functions include minimizing operating costs and improving reliability and flexibility of MGs when faced with uncertainties. In Reference , an MOP based on optimal DFR, re‐phrasing, and optimal DG sizing is proposed in order to minimize active power losses and phase imbalance and enhance the voltage profile. In Reference , an optimal DFR is performed by considering the uncertainties of load demands and output power of DG units.…”

Section: Introductionmentioning

confidence: 99%

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Sedighizadeh

Shaghaghi‐shahr

Aghamohammadi

et al. 2020

Int Trans Electr Energ Syst

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Summary In this article, the optimal distribution feeder reconfiguration (DFR) and optimal generation scheduling (OGS) are simultaneously taken into account to enhance operational flexibility and efficiency in microgrids (MGs). The proposed model considers different features of MGs, including distributed generation (DG) units, plug in hybrid electric vehicles (PHEVs), and demand response (DR) programs. Three DR programs are modeled including, load shifting, load curtailing, and time of use (TOU) programs. Uncertainties of renewable generations and loads are modeled using a computationally competitive approach. Both controlled and uncontrolled charging/discharging patterns of PHEVs are investigated. To enhance tractability, the proposed model is formulated as a mixed‐integer second‐order cone programing (MISOCP) problem to minimize operational cost subject to technical and financial constraints. To evaluate the performance of the proposed model, it is examined on the balanced 33‐bus test systems in different case studies of DFR and OGS. The optimization problem is solved using the GAMS software package. It is found that PHEVs and DR not only enhances MG operation flexibility, but they also reduce the total operation cost and smooth load profile.

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“…For radial distribution systems, Chen and Cherng have suggested a method based on genetic algorithm (GA) to solve the problem of phase rearrangement among distribution transformer banks. In Kaveh et al, a model has been proposed for simultaneous optimization of re‐phasing, reconfiguration, and DG placement in order to decrease the total cost and improve the voltage profile without taking into account the imbalance indices. Similarly, a model has been suggested in Hooshmand and Soltani for phase arrangement of laterals and the distribution transformers based on bacterial foraging (BF) oriented by particle swarm optimization (PSO) algorithm (BF‐PSO).…”

Section: Introductionmentioning

confidence: 99%

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

Homaee

Najafi

Dehghanian

et al. 2019

Int Trans Electr Energ Syst

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Summary Voltage and current imbalance have adverse impacts on power systems such as power loss increase, communication interference, and component lifetime reduction. This paper attempts to look at unbalance impacts from distribution system operator's (DSO) viewpoint in order to understand them and then mitigate the impacts. Since solving the unbalance conditions is influenced by the way the imbalance is defined, standard unbalance indexes are studied, and new indexes are proposed in this paper. Re‐phasing of the customers is selected to reduce the level of unbalance in distribution feeders. Due to the huge number of customers, a wide variety of choices can be selected for re‐phasing of customers, which makes the solution questionable. Therefore, discrete genetic algorithm (DGA) as a metaheuristic method has been utilized in order to distribute customers among the network phases optimally considering the fact that DSO has a limitation for the re‐phasing practice. The aim is to reduce the unbalance indexes and power losses throughout the network. Simulations have been carried out on a real test case network, which shows the importance of load balancing and its effects on the power losses, voltage profile, and current flow in that network. The effectiveness of the proposed indexes has also been demonstrated for the four‐wire multigrounded distribution system. Since re‐phasing of the majority of customers in distribution networks seems impractical, a re‐phasing limitation is also investigated in this paper, and some practical suggestions of optimal load balancing in a real‐world low‐voltage distribution system have been presented here. Results show the importance of load balancing in power loss reduction and voltage unbalance improvement in the low‐voltage four‐wire multigrounded distribution system. They also illustrate that by changing phases of a few customers, power losses and unbalance indexes will be improved significantly.

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Simultaneous optimization of re-phasing, reconfiguration and DG placement in distribution networks using BF-SD algorithm

Cited by 45 publications

References 25 publications

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

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