Optimal siting of DG units in power systems from a probabilistic multi-objective optimization perspective

Dehghanian, Payman; Hosseini, Seyed Hamid; Moeini‐Aghtaie, Moein; Arabali, A.

doi:10.1016/j.ijepes.2013.02.014

Cited by 81 publications

(38 citation statements)

References 37 publications

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“…It is a stochastic or probabilistic methodology. Some other measures including chance-constrained programming [14], blind number theory [51], connection number mode [52] and point estimation [53] are used in the literature. After integrating the distributed generation (DG), there are many more uncertain factors in the planning process.…”

Section: Uncertainty Modelingmentioning

confidence: 99%

Review and prospect of active distribution system planning

Liu

Gao

et al. 2015

J. Mod. Power Syst. Clean Energy

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The approach to planning, design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources (DERs) together with load growth, energy storage technology advancements and increased consumer expectations. Planning of active distribution systems (ADS) has been a very hot topic in the 21st Century. A large number of studies have been done on ADS planning. This paper reviews the state of the art of current ADS planning. Firstly, the influences of DERs on the ADS planning are addressed. Secondly, the characteristics and objectives of ADS planning are summarized. Then, up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation, mathematical model of ADS planning and solution algorithms. Finally, the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS, taking into account of joint planning in secondary system, coordinating goals among different layers, integrating detailed operation simulations and regular performance based reviews into planning, and developing advanced planning tools.

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Section: Uncertainty Modelingmentioning

confidence: 99%

Review and prospect of active distribution system planning

Liu

Gao

et al. 2015

J. Mod. Power Syst. Clean Energy

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“…H(x) is vector of equality constraints and I(x) is vector of inequality constraints. [70] Reduction in Loss and sensitivity factor of active power [84] Minimization of power and feeder loss [50] Maximize profitability and minimize power losses [53] Minimizing investment and operation costs, power loss and the customers energy demand loss [9] Optimizing line loading, losses and reactive power capacity [69] MO Optimizing cost of network upgrading, power losses cost, the cost of energy not supplied and customer energy purchased cost [29] Function of line loading and reactive power losses, minimizing the total power loss [66] Optimization of the network losses and benefit/cost relation [62] Minimization of system losses, network disruption cost and maximizing DG rating [85] Dynamic SO Saving in electricity bills [38] The difference between benefits & costs [87] Energy losses [54,86] Loss adjustment factor & individual generation load factor [86] Category Type…”

Section: Subject To the Constraintsmentioning

confidence: 99%

A review on optimal sizing and siting of distributed generation system: Integrating distributed generation into the grid

Gupta

Pandit²,

Kothari³

2014

2014 6th IEEE Power India International Conference (PIICON)

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The non-conventional resources like sun and wind are never-ending, reliable and pollution less sources of power. When such limited size renewable sources situated close to load centers are used for generating power, they are termed as distributed energy resources (DER) or Distributed Generation (DG). After the Kyoto protocol on climate change there is thrust to preserve depleting reserves of conventional fuels i.e. coal and petroleum. One of the ways to achieve the same is through integrating these DER into the present electric supply system. The DER can be used in isolation or in integration with the existing system. Integration of DER with the grid supply helps to provide continuous supply of electricity with overall improvement in the system performance. The capacity sizing and siting ( also called location allocation) of the DG is an important consideration for economic operation with lowest investment. It is a large scale non linear multi-objective constrained optimization problem with high dimension and practical constraints. A review on optimal sizing and siting of DG system has been attempted in this paper. The various solution techniques and objective functions along with various constraints applicable for the solution of optimal sizing and siting problem of DG are discussed and compared in this paper.

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“…Axial compressor blade shape optimization was performed by multi-objective optimization by Samad and Kim [16,17]. The NSGA-II algorithm is also successfully applied in various power system problems such as multi-objective siting of DG units in power system [18], multi-objective generation expansion planning [19], coordinate the charging process of plug-in hybrid electric vehicles (PHEVs) in the context of energy hubs [20] and reactive power planning [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of wind turbine blades using lifting surface method

Shen

Chen

Zhu

et al. 2015

Energy

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a b s t r a c tThis paper describes a multi-objective optimization method for the design of horizontal axis wind turbines using the lifting surface method as the performance prediction model. The aerodynamic code for the design method is based on the lifting surface method with a prescribed wake model for the description of the wake. A multi-objective optimization algorithm approach is employed for the optimization of wind turbine blades with 3D stacking line (swept leaned blades). The (NSGA II) Nondominated sorting genetic algorithm II is used to facilitate the multi-objective optimization and to find the global optima of high-dimensional problems. The scope of the optimization method is to achieve the best trade off of the following objectives: maximum of annual energy production and minimum of blade loads including thrust and blade rood flap-wise moment. To illustrate how the optimization of the blade is carried out the procedure is applied to NREL Phase VI rotor. The result shows the optimization models can provide more efficient designs.

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Optimal siting of DG units in power systems from a probabilistic multi-objective optimization perspective

Cited by 81 publications

References 37 publications

Review and prospect of active distribution system planning

Review and prospect of active distribution system planning

A review on optimal sizing and siting of distributed generation system: Integrating distributed generation into the grid

Multi-objective optimization of wind turbine blades using lifting surface method

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