Optimisation of embedded generation sizing and siting by using a double trade-off method

Carpinelli, G.; Celli, Gianni; Mocci, Susanna; Pilo, Fabrizio; Russo, Angela

doi:10.1049/ip-gtd:20045129

Cited by 146 publications

(63 citation statements)

References 11 publications

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“…MODGP formulation based on GA and ε constrained method is proposed to solve the best compromise (tradeoff) solution for DM (DISCO). [22] 2005 3 1 ✓ ---Similarly, with the same formulation (GA and ε constrained method), a double tradeoff method is presented to find the best alternative. [23] 2007 2 1 ✓ ---MCS embedded in GA planning methodology is proposed to improve the accuracy for stochastic DG integration with tradeoff solution.…”

Section: Planning Techniques Based On Evaluation Methods In Multi-objmentioning

confidence: 99%

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Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

2017

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Distribution networks (DNWs) are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP) strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN) needs to consider choices, primarily distributed generation (DG), network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP) in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs) based on mult-objective optimizations (MOOs) in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT) have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at the multi-objective-based frameworks, are also presented in this paper.Keywords: active distribution network (ADN); distributed generation (DG); distributed energy resources (DERs); distribution network planning (DP); multi-objective optimization (MOO); multi-criteria decision analysis (MCDA); distributed generation placement (DGP); volt-ampere reactive power (VAR) compensation and power quality (VPQ); component reinforcement and up gradation (CRU); network (distribution) topology change and reconfiguration (NTR); planning techniques (PT); multiple objective planning (MOP); multi-objective planning techniques (MOPTs); future distribution networks (FDNs)

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Section: Planning Techniques Based On Evaluation Methods In Multi-objmentioning

confidence: 99%

“…The ε-constrained method [21,22]; MCS [23]; goal programming [26,32]; fuzzy, WSM [29]; MCS, AHP [30]; PSO [31] and multi-criteria stochastic programming model (MSPM) [34]; VPQ (B): GA based fuzzy multi-objective method [54,60].…”

Section: Hybrid Methodsmentioning

confidence: 99%

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

2017

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“…Multi-objective optimization problem has been solved by ε-constrained technique in [16][17][18] to suggest optimal allocation of DG in which one objective is selected as main objective and rest of objectives are treated as constraints. Minimization of cost of energy losses, voltage profile and power quality index are selected as objectives in [16] to attempt optimization by ε-constrained technique.…”

Section: Introductionmentioning

confidence: 99%

“…Minimization of cost of energy losses, voltage profile and power quality index are selected as objectives in [16] to attempt optimization by ε-constrained technique. The ε-constrained technique has been combined with GA [17] for optimal allocation of DG by minimizing four costs network upgrading, purchased energy, energy losses, energy not supplied.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Approach for Placement of Multiple DGs for Voltage Sensitive Loads

Kaur

Jain

2017

Energies

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This paper presents the optimal placement of multiple Dispersed Generators using multi-objective optimization. The optimization is carried out with objectives namely active power loss, reactive power loss, voltage deviation and overall economy. The multi-objective optimization and accounting conflicting objectives are realized through Particle Swarm Optimization with fuzzy decision approach to find the optimal sizes and sites of Dispersed Generators for voltage dependent residential, commercial and industrial loads. The clusters of buses are formulated from base case load flow to limit the search space for finding the placement of the Dispersed Generators. The effectiveness of the proposed approach is tested on a 69-bus radial distribution. It is found that the optimal placement of the Dispersed Generators improves the overall performance of the system and the optimal allocation is affected by the type of load.

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“…ElKhattam et al [4] proposed heuristic approach for planning of DG capacity to maximize benefits to distribution companies. Carpinelli et al [5] developed an ε-constrained technique to suggest allocation of dispersed generation in presence of uncertainties to minimize cost of energy losses, voltage profile and total harmonic distortion. An algorithm using combinational GA and ε-constrained technique is proposed by Celli et al [6] to determine location and size of distributed generators by minimizing different functions related to cost of energy losses, cost of service interruptions, cost of network upgrading and cost of energy purchased.…”

Section: Introductionmentioning

confidence: 99%

Analytical Approach for Optimal Allocation of Distributed Generators to Minimize Losses

Kaur¹,

Jain²

2016

Journal of Electrical Engineering and Technology

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-In this paper the integration of Distributed Generation (DG) in radial distribution system is investigated by computing the optimal site and size of DG to be placed. An analytical expression based on equivalent current injection has been derived by utilizing topological structure of radial distribution system to find optimal size of DG to minimize losses. In the presented formulation, the optimal DG placement is obtained without repeatedly computing the load flow. The proposed formulation can be used to find the optimal size of all types of DGs namely Type-I, Type-II, Type-III and Type-IV DGs. The investigations are carried out on IEEE 33-bus and 69-bus radial distribution systems. The optimal DG placement results into reduction in active and reactive power losses and improvement in voltage profile of the buses.

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Optimisation of embedded generation sizing and siting by using a double trade-off method

Cited by 146 publications

References 11 publications

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

Multi-Objective Optimization Approach for Placement of Multiple DGs for Voltage Sensitive Loads

Analytical Approach for Optimal Allocation of Distributed Generators to Minimize Losses

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