An investigation of reactive power planning based on chance constrained programming

Yang, Ning; Yu, C.W.; Wen, Fushuan; Chung, C.Y.

doi:10.1016/j.ijepes.2006.09.008

Cited by 67 publications

(25 citation statements)

References 13 publications

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“…The concept is based on converting the probabilistic statements to deterministic equivalents so as to make them algebraically operable. An extensive use of the said approach is found in various engineering fields such as reactive power planning [42], transmission system expansion planning [43], community scale renewable energy system planning [44], etc. In this paper, the system reliability in terms of confidence level is validated through a sequential Monte-Carlo simulation of the system.…”

Section: Nomenclaturementioning

confidence: 99%

Optimum sizing of wind-battery systems incorporating resource uncertainty

2010

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Section: Nomenclaturementioning

confidence: 99%

Optimum sizing of wind-battery systems incorporating resource uncertainty

2010

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“…Kim et al developed a TSP model for optimal designing hydrogen supply chains, which consisted of various activities such as production, storage and transportation [250]. Yang et al proposed a new method for optimal transmission system expansion planning based on CCP with the consideration of several uncertain factors such as the locations and capacities of new power plants as well as demand growth [251]. Arun et al adopted a CCP approach for incorporating uncertain energy resources into a facility sizing and design system [252].…”

Section: Stochastic Mathematical Programmingmentioning

confidence: 99%

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

et al. 2011

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Abstract:Energy is crucial in supporting people's daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in OPEN ACCESSEnergies 2011, 4 1625 uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.

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“…An artificial bee colony based optimization technique for the optimal power flow problem and simultaneous reduction of active power loss in a large power network is described in [20]. Chance constrained programming based reactive power planning is investigated in [21]. How various optimization techniques can be used to overcome the different problems associated to reactive power planning is discussed in [22].…”

Section: Introductionmentioning

confidence: 99%

GA based optimal planning of VAR sources using Fast Voltage Stability Index method

Bhattacharyya¹,

Rani²,

Vais³

et al. 2016

Archives of Electrical Engineering

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This paper presents a novel approach for reactive power planning of a connected power network. Reactive power planning is nothing but the optimal usage of all reactive power sources i.e., transformer tap setting arrangements, reactive generations of generators and shunt VAR compensators installed at weak nodes. Shunt VAR compensator placement positions are determined by a FVSI (Fast Voltage Stability Index) method. Optimal setting of all reactive power reserves are determined by a GA (genetic algorithm) based optimization method. The effectiveness of the detection of the weak nodes by the FVSI method is validated by comparing the result with two other wellknown methods of weak node detection like Modal analysis and the L-index method. Finally, FVSI based allocation of VAR sources emerges as the most suitable method for reactive power planning. Key words: active power loss, FVSI method, genetic algorithm, operating cost, reactive power planning, weak nodes IntroductionReactive power planning has been one of the most challenging problem to the power system engineers. Expansion of a transmission network is not always possible because of several reasons like cost, right of way etc. Again maintaining good quality of voltage for distribution of power to the consumer is also an important aspect. Sometimes a situation of system collapse is observed even with the slight increase in reactive power demand. Hence, it becomes absolutely necessary for the proper co-ordination and planning of all reactive power sources present in the network. In this paper, main concerns are proper planning and coordination of control variables like transformer tap changers, shunt VAR compensators, generators reactive VAR in an interconnected power system for minimum active power loss as well as minimum operating cost.A linear programming based optimization method is presented in [1] for optimal reactive power generation of large scale electric power networks. The solution of the reactive power problem by optimal placement of a capacitor is discussed in [2]. A modal analysis technique is described in [3] for the determination of weak nodes of a connected power network. The reactive power problem is solved in [4] by injecting reactive power at weak buses of a connected power system. Application of evolutionary programming in reactive power planning is discussed in [5]. A hybrid expert system and simulated annealing based algorithm for the planning of reactive power sources is presented in [6]. A technique for the determination of reactive margin is presented in [7]. Heuristic modal for secured operation of a power system is developed in [8]. A binary search technique and special heuristics are applied for optimal VAR planning in the case of a large scale power system in [9]. Static security constraints and nonprobabilistic uncertainties in load values are considered for optimal reactive power planning in [10]. Two rule based modules are used for voltage profile improvement to alleviate voltage limit violations and for minimization of ...

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An investigation of reactive power planning based on chance constrained programming

Cited by 67 publications

References 13 publications

Optimum sizing of wind-battery systems incorporating resource uncertainty

Optimum sizing of wind-battery systems incorporating resource uncertainty

A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty

GA based optimal planning of VAR sources using Fast Voltage Stability Index method

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