A mixed-integer LP model for the optimal allocation of voltage regulators and capacitors in radial distribution systems

Franco, John F.; Rider, Marcos J.; Lavorato, Marina; Romero, Rubén

doi:10.1016/j.ijepes.2012.11.027

Cited by 90 publications

(82 citation statements)

References 25 publications

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“…The PT categorizations have designated in this paper by DG placement (DGP) . Followed by VAR compensation and power quality (VPQ) [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]. Moreover, component (protection and automation devices) placement, modern grid reinforcement and upgrades (CRU) [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89].…”

Section: Paper Contributionmentioning

confidence: 99%

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: Paper Contributionmentioning

confidence: 99%

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

2017

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“…A detailed analysis of capacitor and AVR placement was discussed in [8]; some years later, another well-known work [9] presented a mathematical formulation for optimal capacitor placement, modeled as a nonlinear mixed-integer programming problem. Proposals using multiobjective optimization have arisen recently: the authors of [10] have presented a tabu-search-based approach to capacitor placements, trying to solve the conflict between costs and loss reductions; in [2], a solution has been presented for the capacitor allocations; however, taking into account the CVR, the objectives to be minimized were costs, voltage deviation, and the reduction of losses and demand; [11] shows a micro-genetic algorithm to AVR allocations, and the objectives were the power losses and the reductions in voltage drops; in [12], a mixed-integer linear programming model is used that considers the costs and the voltage deviations as the objectives to allocate the AVRs; in [13], a genetic algorithm is employed to simultaneously solve the planning problem of capacitor and AVRs placement, considering the losses, the voltage deviation, and the costs as objectives, and in [14], a nondominated sorting genetic algorithm is presented for the allocation of capacitors and AVRs and for cable replacement, considering costs and the voltage deviations.…”

Section: Volt-var Optimization Cvr and Loadsmentioning

confidence: 99%

“…The inequalities are represented by voltage limits (regulatory voltage quality conditions) on all buses, and power factor limits (technical and economic operational conditions) at the substation of the system, as shown in (11) and (12), respectively (11) (12) Transformers with tap variables and voltage regulators are devices which may control the output voltage through the load tap-changing mechanism. Hence, these devices can lead to variations of the output voltage in steps ( ), values in per unit or percent, with a specified number of tap positions.…”

Section: B Constraintsmentioning

confidence: 99%

Volt-VAR Multiobjective Optimization to Peak-Load Relief and Energy Efficiency in Distribution Networks

Padilha‐Feltrin

Rodezno

Mantovani

2015

IEEE Trans. Power Delivery

107

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This paper addresses the integrated volt-var control for distribution network operation via multiobjective optimization. This paper seeks to explore the problem of energy savings and peak demand relief through the voltage reduction procedure. Currently, due to the emergence of the distribution smart grids, these procedures are gaining renewed interest and attention. The proposal presented here is for the operation phase, with a strategy based on an hourly load forecast for the next day/week, taking into account the active power intake reduction, and the voltage deviation. Therefore, the result is a set of nondominated optimal solutions, and then one may decide when, where, and how to apply them to meet different goals. The obtained solutions, for two typical distribution networks, describe relevant economic and technical benefits. Index Terms-Distribution systems, integrated volt-var control, multiobjective optimization, voltage reduction. NOMENCLATURETotal energy savings.Voltage drop at the substation bus.Active load power at bus number .Active load power at the rated voltage and frequency at bus .Reactive load power at bus number .Reactive load power at the rated voltage and frequency at bus . andVoltage exponents of active and reactive load power.Voltage magnitude at bus number .Nominal voltage of the system. Voltage drop between buses and .Line resistance between buses and .Line impedance between buses and .

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“…(4.44) follows standard linear programming form, except for the last constraint which does not take a linear form. However, this constraint can be easily linearized by introducing for each time point ti, a variable array which is integer; this is why the optimization formulation is a mixed integer linear program [32].…”

Section: Energy Management Layermentioning

confidence: 99%

Optimal Energy Management

Mahmoud

AL-Sunni

2015

Power Systems

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The increasing incidence of distributed generation (DG) and active distributed networks, changing regularities, and needs to improve the power system reliability and clean power support is providing development of a new power system perception commonly referred to as the smart grid (SG). In this regard, the microgrid (MG) can be considered as one of the most promising concepts. A MG is essentially an active distribution network defined as an integrated power delivery system. A MG consists of a low-voltage (LV) network composed of loads, renewable energy sources (RESs), and DG units operating as a single controllable load connected to the main grid. Compared to the conventional power plants, a MG is characterized by specific operation and constraints depending on several critical stochastic parameters. So far, MGs have been mostly established as test-bed platforms in some developed countries such as Japan, Canada, and the U.S. In fact, such concept cannot be widely implemented in practice without a prior successful establishment of an energy manager to achieve optimal and reliable control of the MG for a given site to minimize its operation cost while consolidating its reliability and environment-friendly features [1, 5-7, 12, 17, 23-27, 31].As an essential element in the new era of smart power, several approaches have been reported in the literature in relation to MG intelligent energy management applicable within the SG system [22,56]. A fuel consumption minimization approach has been proposed in [47] based on power sharing approach, in which the optimized cost function includes a penalty function for heat generation excess. The authors promoted the importance of a MG communication infrastructure allowing the coordination with RES forecasting as a part of the central control. However, the proposed approach does not take into account the prediction of RE power generation and the management of storage devices. In [76], mesh-adaptive direct-search-based optimization algorithm is applied for MG online energy management; however, this approach does not provide information regarding the forecasting of the RESs or load demand (LD). Moreover, the MG energy management problem has been reduced to

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A mixed-integer LP model for the optimal allocation of voltage regulators and capacitors in radial distribution systems

Cited by 90 publications

References 25 publications

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

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

Volt-VAR Multiobjective Optimization to Peak-Load Relief and Energy Efficiency in Distribution Networks

Optimal Energy Management

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