Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations

Zarei, Milad; Zangeneh, Ali

doi:10.1002/etep.2425

Cited by 20 publications

(18 citation statements)

References 23 publications

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“…The above literature, however, has not taken into account the uncertainty of parameters in their formulation. However, the uncertainty of wind power is considered in [17,18] using the point estimate method in single/multi-objective problems. It is worthy to note that the point estimate method needs lots of information about the uncertain parameter.…”

Section: Der Utilisationmentioning

confidence: 99%

Risk averse energy management strategy in the presence of distributed energy resources considering distribution network reconfiguration: an information gap decision theory approach

Nikkhah

Rabiee

Mohseni‐Bonab

et al. 2020

IET Renewable Power Generation

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Distributed energy resources (DERs) and distribution network reconfiguration have considerable effects on both the economic and operational performance of distribution networks. However, the uncertain nature of renewable energy sources (RESs), wind energy, for instance, can bring about serious challenges to the distribution system operators and distribution companies (DisCos). Therefore, a suitable methodology is a matter of the utmost importance to handle the uncertainty of RESs. In addition, DisCos can benefit from the utilisation of energy storage technologies to increase the penetration of RESs into the system. In this regard, this study proposes a risk-averse energy management strategy (RA-EMS) in the presence of DERs, while the impact of uncertainties of RESs on the optimal configuration of the network is investigated. The uncertainty of RESs is modelled through the information gap decision theory, which has significant advantages such as low computational burden, no need for probability density function, and exact results compared to other methodologies for uncertainty handling. The proposed RA-EMS model is implemented on the IEEE 33-bus distribution system, and its superiority over the scenario-based stochastic programming is demonstrated. The robust configuration of the system against RESs' uncertainty is obtained for different levels of uncertainty radius.

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Section: Der Utilisationmentioning

confidence: 99%

Risk averse energy management strategy in the presence of distributed energy resources considering distribution network reconfiguration: an information gap decision theory approach

Nikkhah

Rabiee

Mohseni‐Bonab

et al. 2020

IET Renewable Power Generation

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“…In Reference , a new voltage stability index is maximized for the optimization of the DFR. In Reference , an MOP is presented with the purpose of DFR to lower costs of active power losses and energy not supplied and to increase voltage stability and DG penetration level. A two‐point estimation technique is used to model the uncertainties of electric demands and power generated by WTs.…”

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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“…However, both the studies did not consider renewable DG and reconfiguration. Researchers have also used other optimization techniques such as simulated annealing, artificial immune algorithm, vaccine‐enhanced artificial immune system, modified plant growth simulation algorithm, PSO, GA, runner root algorithm, harmony search algorithm, artificial bee colony algorithm, hybrid Harmony search and particle artificial bee colony algorithm, interval analysis, stochastic MILP, Ant Colony Optimization, hybrid receding horizon control and scenario analysis, nondominated sorting GA, fuzzy mutated GA, tabu search, Benders decomposition approach, and evolutionary programming . Summary of the reviewed literature is presented in Tables and .…”

Section: Introductionmentioning

confidence: 99%

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

Int Trans Electr Energ Syst

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Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

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Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations

Cited by 20 publications

References 23 publications

Risk averse energy management strategy in the presence of distributed energy resources considering distribution network reconfiguration: an information gap decision theory approach

Risk averse energy management strategy in the presence of distributed energy resources considering distribution network reconfiguration: an information gap decision theory approach

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

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

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