Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method

Alavi, Seyed Arash; Ahmadian, Ali; Golkar, Masoud Aliakbar

doi:10.1016/j.enconman.2015.02.042

Cited by 190 publications

(109 citation statements)

References 29 publications

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“…In our study, the robust optimization concept is also used to investigate the uncertainty of the multienergy management problem. In contrast to such conventional robust techniques [19][20][21], a distinguishing feature of the proposed method is that a data-driven modeling technique for the uncertainty is used to establish an ambiguity set based on the available data. Recently, data-driven robust optimization has been proposed to overcome the deficiency of solution conservation by considering the information knowledge available in the statistical data.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Han

Yang

Sun

et al. 2019

IEEJ Transactions Elec Engng

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To facilitate energy system integration, it is imperative that a multienergy system produce and deliver in a coordinated way the energy in its different component forms. In particular, a small-scale integrated energy system must accommodate renewable energy resources, flexible loads, and energy coupling technologies, which creates new challenges to the interactions between the energy vectors. Hence, an energy management model for a microenergy system in grid-connected mode under uncertainties is proposed to perform the decision-making for shifting energy modes for all energy sources and end-use applications with the aim of optimally scheduling controllable energy resources in the system and minimizing the net management cost under uncertainty. To address the stochastic nature of the price of electricity, a data-driven robust optimization approach is introduced. It uses the available sample data to design the appropriate uncertainty set using statistical hypothesis tests, and a combination of conditional value-at-risk and a worst-case optimization problem is used to formulate the energy management problem under uncertainty. We explore a computationally tractable robust counterpart of the original optimization problem. The optimal energy scheduling solution obtained from the proposed approach is immune against any worst-case realizations. Numerical results demonstrate the effectiveness of the proposed approach and its performance of less conservation.

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Section: Introductionmentioning

confidence: 99%

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Han

Yang

Sun

et al. 2019

IEEJ Transactions Elec Engng

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“…In the initial search, the particles should be more learning to their own, less to learn from the best global. Global search ability of particles is reinforced, which avoids falling into local optimum searching early, so 1 c is bigger value, 2 c is smaller value. While in the latter part of the search, in order to make the particles closer to the global optimum, and enhance the local search ability …”

Section: Adaptive Chaos Particle Swarm Optimizationmentioning

confidence: 99%

“…Distributed Generation (DG) access to the normal operation of the distribution network had a significant impact on increasing the distribution network planning difficulties and uncertainties, to the distribution network operation and planning challenges [2].…”

Section: Introductionmentioning

confidence: 99%

Uncertain Reasoning of Distribution Network Planning for Distributed Generator

Yan¹,

Wang²

2016

Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering

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Abstract-In order to study the impact of distributed generation (DG) in distribution network planning, a fuzzy programming approach is proposed. Triangular fuzzy numbers are used to describe the uncertainty of DG. Based on credibility theory, the network planning model is established. Fuzzy power flow is calculated, and the fitness function is determined based on the power flow results, and genetic algorithm (GA) is proposed to solve the model. The simulation results of the proposed approach are compared with other planning methods, the effectiveness of the propose approach is proved.

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“…To accomplish the planned optimal operation, distributed controllable loads such as battery and heat pump are used. In 2015, Alavi et al [71] proposed an optimal approach for operating a MG in islanded mode. The uncertainties associated with output of renewable resources and demands are demonstrated by point estimate method (PEM) and robust optimization (RO), respectively.…”

Section: 2mentioning

confidence: 99%

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

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The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

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Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method

Cited by 190 publications

References 29 publications

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Modeling the Operation of Small‐Scale Integrated Energy Systems Based on Data‐Driven Robust Optimization

Uncertain Reasoning of Distribution Network Planning for Distributed Generator

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

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