An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances—Part II: Methodology

Muzhikyan, Aramazd; Farid, Amro M.

doi:10.1109/tie.2015.2395391

Cited by 29 publications

(42 citation statements)

References 43 publications

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“…The objective function of UC as a well-known problem in power system operation is commonly modeled as (1a) which includes the energy production costs presented in (1b) and start-up/shut-down costs provided in (1c) and (1d) [2]- [6], [13], [16]. …”

Section: A Operational Cost Objective Functionmentioning

confidence: 99%

“…System operator needs to have sufficient up-anddown SR to properly manage power systems' uncertainties and contingencies. To the best of our knowledge, the previous works in the area of unit commitment (UC) are related to solution methodologies for UC [2]- [6], risk-constrained UC and modeling related uncertainties [6]- [9], and scheduling of sufficient reserve [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…A hybrid genetic algorithm and differential evolution, particle swarm optimization, Matroid theory, mixed integer linear programming and enterprise control techniques are developed in [2]- [6] to solve different UC problems.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the approaches presented in [7]- [12] are only focused on single objective optimization problem in their evaluations [7]- [12]. Additionally, the developed frameworks in [1]- [6], [13] have not delved into the probabilistic treatment of the uncertain and stochastic factors existing in the decision making under study. To this end, a new meta-heuristic bi-objective algorithm, named non-dominated sorting backtracking search optimization (NSBSO), is modeled to cope with the biobjective optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

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Abstract-Large penetration of wind generating units in power systems necessitates a flexible unit commitment tool to handle the intermittent nature of these units as well as demand. Moreover, power system operators face not only the risks of wind power curtailments, but also probable unit outages. Therefore, assessing a trade-off between operational costs and such risks is very important. In the proposed approach, the probability of the residual demand falling within the up-and-down spinning reserve imposed by n-1 security criterion is converted into a risk index. A new bi-objective probabilistic risk/cost based unit commitment model is proposed to simultaneously minimize both the operational costs and risk. The novel formulation presented provides a new power re-dispatch process to satisfy up-and-down ramp rate constraints. A new operational cycles based unit commitment algorithm is developed. The approach profits from a new non-dominated sorting backtracking search optimization algorithm for extracting the Pareto optimal set. The proposed approach is shown to provide superior results when applied to two test systems: a) 10-unit and b) IEEE 118-bus, 54-unit system.

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Section: A Operational Cost Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

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“…Generally speaking, each of these layers have typically been addressed individually despite their interdependence. More recent work instead advocates a holistic enterprise control approach [31,69,70] where all three layers are simultaneously synthesized, analyzed and simulated.…”

Section: Introduction: Resilience In Power System Coordination and Controlmentioning

confidence: 99%

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

Farid

2015

Intell Ind Syst

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Recently, the academic and industrial literature has coalesced around an enhanced vision of the electric power grid that is intelligent, responsive, dynamic, adaptive and flexible. One particularly emphasized "smart-grid" property is that of resilience where healthy regions of the grid continue to operate while disrupted and perturbed regions bring themselves back to normal operation. Multi-agent systems have recently been proposed as a key enabling technology for such a resilient control scheme. While the power system literature has often addressed multi-agent systems, many of these works did not have resilience as the central design intention. This paper now has a two-fold purpose. First, it seeks to identify a set of multi-agent system design principles for resilient coordination and control of future power systems. To that end, it draws upon an axiomatic design for large flexible engineering systems model which was recently used in the development of resilience measures. From this quantitative model, a set of design principles are easily distilled. Second, the paper assesses the adherence of existing multi-agent system implementations with respect to these design principles. The paper concludes that while many multi-agent systems have been developed for power grids, they have been primarily intended as the decentralization of a particular decision-making/control algorithm. Thus many of the works make only limited contributions to power grid resilience.B Amro M. Farid

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eIoT

Muhanji¹,

Flint²,

Farid³

2019

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adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

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An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances—Part II: Methodology

Cited by 29 publications

References 43 publications

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Multi-Agent System Design Principles for Resilient Coordination & Control of Future Power Systems

eIoT

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