Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

Liu, Zhipeng; Wen, Fushuan; Ledwich, Gerard

doi:10.1109/tpwrd.2011.2165972

Cited by 388 publications

(225 citation statements)

References 25 publications

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“…Since the losses of the DSSs are also minimized in the objective function, the relaxed inequality constraint is the same as the original equality constraint. Finally, constraints (14) and (15) define the ramp-up and ramp-down limits of the DSS power capability.…”

Section: ) Dsss Installation Constraintsmentioning

confidence: 99%

“…The subject of DG siting and sizing have been largely addressed in the literature (e.g., [11]- [14]). In particular, optimal siting and sizing of DGs with the goal of minimizing network losses has been addressed in [11].…”

Section: Introductionmentioning

confidence: 99%

“…In [12], the problem of optimal DG placement is investigated within the specific context of deregulated electricity markets with the aim of maximizing the social welfare and profits of DG owners. A multi-objective approach taking into account investment and maintenance of DG together with network operation, losses, and capacity adequacy costs is used in [13] and [14] for the optimal siting and sizing of DGs.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Nick

Cherkaoui

Paolone

2014

IEEE Trans. Power Syst.

378

221

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Abstract-Dispersed storage systems (DSSs) can represent an important near-term solution for supporting the operation and control of active distribution networks (ADNs). Indeed, they have the capability to support ADNs by providing ancillary services in addition to energy balance capabilities. Within this context, this paper focuses on the optimal allocation of DSSs in ADNs by defining a multi-objective optimization problem aiming at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedure accounts for: 1) network voltage deviations; 2) feeders/lines congestions; 3) network losses; 4) cost of supplying loads (from external grid or local producers) together with the cost of DSS investment/maintenance; 5) load curtailment; and 6) stochasticity of loads and renewables productions. The DSSs are suitably modeled to consider their ability to support the network by both active and reactive powers. A convex formulation of ac optimal power flow problem is used to define a mixed integer second-order cone programming problem to optimally site and size the DSSs in the network. A test case referring to IEEE 34 bus distribution test feeder is used to demonstrate and discuss the effectiveness of the proposed methodology.Index Terms-Ancillary services, energy storage, mixed integer second order cone programming, power distribution, power system planning. NOMENCLATURE: DSSDispersed storage system. DG Distributed generation.Variables: 1 Binary variable associated with the presence of energy storage at bus .

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Section: ) Dsss Installation Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Nick

Cherkaoui

Paolone

2014

IEEE Trans. Power Syst.

378

221

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“…4, pp. 2541-2551, Oct. 2011 M. Moradi, M. Hajinazari, S. Jamasb, and M. Paripour, -An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming, ‖ Energy, vol. 49, pp.…”

Section: Discussionmentioning

confidence: 99%

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Luo¹,

Gu²,

Gao³

et al. 2017

JOCET

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Abstract-The development of combined cooling, heating, and power (CCHP) microgrids using distributed cogeneration equipment and renewable energy sources has drawn considerable research attention. In this study, we propose an interval mixed-integer linear programming (IMILP) dispatching model for supporting the economic dispatch of a CCHP microgrid under uncertainties. Our model is based on the interval programming method, where uncertainties can be incorporated and communicated into the economic dispatch problem using interval values. The proposed IMILP dispatching model is decomposed into two sets of deterministic sub-models, f -and f + , which are solved sequentially. We use a case study of a hotel in China to demonstrate how this model can be applied to support economic dispatch decisions for a CCHP microgrid. Simulation results indicated that load and PV power uncertainties mainly affect the dispatching results of the power exchange, gas boiler and electricity chiller, while the power generation unit is consistent under different uncertainty levels. The obtained dispatching results can provide more flexibility for decision makers by generating alternative decisions for CCHP microgrid dispatching under uncertainties.Index Terms-CCHP, economic dispatch, interval programming, microgrid, uncertainty. I. INTRODUCTIONThe energy crisis and rising air pollution have led to greater worldwide focus on energy efficiency methods. Combined cooling, heating, and power (CCHP) systems are also referred to tri-generation systems. They have been widely used in small-medium scale power systems to solve energy-related problems, including increasing energy demands, higher energy costs, energy supply security, and environmental concerns [1]- [6]. A CCHP microgrid integrates power generation units (PGUs, e.g. Fuel cell or Micro turbine), auxiliary boilers, heat recovery systems, refrigeration equipment (electric chiller or absorption chiller), energy storage devices (heat storage tanks or storage batteries), and renewable energy sources (e.g. photovoltaic, wind, biomass) to implement the cascade utilization of energy. The main difference between CCHP microgrid and traditional CCHP systems is that the former can not only satisfy the cooling, Manuscript received August 28, 2015; revised February 25, 2016. This work was supported by the National High Technology Research and Development Program of China (863 Program, grant no. 2011AA05A107), the National Science Foundation of China (grant no. 51277027), the Natural Science Foundation of Jiangsu Province of China (grant no. SBK201122387), the State Grid Corporation of China.Zhao Luo, Wei Gu, Zhihe Wang, and Yiyuan Tang are with the School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China (e-mail: waiting.1986@live.com).Song Gao is with Jiangsu Electric Power Company, Nanjing, Jiangsu 210024, China.heating, and power demands of certain types of customers (such as office buildings, hotels, schools, commercial malls, and industrial loads), but also interacts ...

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“…Dada su ubicación cerca de la demanda, la GD puede contribuir a la disminución de pérdidas activas y al mejoramiento del perfil de tensiones (Porkar et al, 2010). Adicionalmente, estudios recientes han mostrado que la GD puede traer otros beneficios como el aplazamiento de inversiones en la red (Gautam & Mithulananthan, 2007), el aumento en la confiabilidad (Jabr y Pal 2009) y la reducción de congestión (Liu et al 2011). Sin embargo, los beneficios de la GD dependen en gran medida de su ubicación y dimensionamiento.…”

Section: Introductionunclassified

Ubicación de Generación Distribuida para Minimización de Pérdidas Usando un Algoritmo Genético Híbrido

Narváez¹,

López-Lezama

Velilla

2015

Inf. tecnol.

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ResumenEn este artículo se presenta una metodología para la ubicación óptima de Generación Distribuida (GD) usando un algoritmo genético híbrido. En este caso la función objetivo es la reducción de pérdidas. El algoritmo propuesto incorpora una red neuronal artificial para evaluar la función de adaptación y una búsqueda local que permite al algoritmo explorar un espacio de búsqueda más amplio. La contribución principal del artículo es la combinación de técnicas metaheurísticas con técnicas de inteligencia artificial para resolver un problema de optimización multi-modal y no convexo. La metodología propuesta es probada en un sistema de 34 barras mostrando que la ubicación apropiada de las unidades de GD permite reducir las pérdidas activas y mejorar el perfil de tensiones. Palabras clave: algoritmos genéticos, generación distribuida, redes neuronales artificiales Location of Distributed Generation for Power Losses Reduction using a Hybrid Genetic Algorithm AbstractThis paper presents a methodology for optimal Distributed Generation (DG) allocation using a hybrid genetic algorithm. In this case, the objective function is the minimization of power losses. The proposed algorithm involves an artificial neural network to evaluate the fitness function and a local search subroutine that allows the algorithm to explore a wider search space, and eventually, escape from local optimal solutions. The proposed approach was tested on a 34-bus system showing that the proper allocation of DG units allows the reduction of active power losses and the improvement of voltage profile.

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Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Uncertainties

Cited by 388 publications

References 25 publications

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Ubicación de Generación Distribuida para Minimización de Pérdidas Usando un Algoritmo Genético Híbrido

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