Stochastic optimization of hybrid renewable energy systems using sampling average method

Sharafi, Masoud; ElMekkawy, Tarek Y.

doi:10.1016/j.rser.2015.08.010

Cited by 72 publications

(22 citation statements)

References 23 publications

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“…In this study, we review the literature on GC decentralized HRESs and categorize the problems as single objective() and multi‐objective. () The review summary can be found in Table .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Then, the problem is solved using a variant of Benders' decomposition method. Sharafi and ElMekkawy include the stochasticity of renewable resources and variability in demand into the system that they propose in Sharafi et al Pareto front is approximated using a simulation module, DMOPSO algorithm, and sampling‐average method. The authors have 3 objectives: maximizing the renewable energy ratio, minimizing total net present cost, and minimizing fuel emission.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To sum up, the 2 aspects that make these optimal design problems complex (their multi‐objective and stochastic natures) should be considered to obtain more realistic results. Yet to the best of our knowledge, the work of Sharafi and ElMekkawy is the only study in the literature that considers a multi‐objective design problem of a GCDES while handling the uncertainty related to renewable resources. We pursue a similar line of research but focus on a generic system, which includes all the challenges related to the system design.…”

Section: Literature Reviewmentioning

confidence: 99%

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Bi-objective optimization of a grid-connected decentralized energy system

et al. 2017

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Summary Motivated by the increasing transition from fossil fuel–based centralized systems to renewable energy–based decentralized systems, we consider a bi‐objective investment planning problem of a grid‐connected decentralized hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. A national grid is assumed to be a carbon‐intense alternative to the renewables and is used as a backup source to ensure reliability. We consider both total cost and carbon emissions caused by electricity purchased from the grid. We first discuss a novel simulation‐optimization algorithm and then adapt multi‐objective metaheuristic algorithms. We integrate a simulation module to these algorithms to handle the stochastic nature of this bi‐objective problem. We perform extensive comparative analysis for the solution approaches and report their performances in terms of solution time and quality based on well‐known measures from the literature.

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“…In this study, we review the literature on GC decentralized HRESs and categorize the problems as single objective() and multi‐objective. () The review summary can be found in Table .…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Bi-objective optimization of a grid-connected decentralized energy system

et al. 2017

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“…Ref [13] applied CCP to design a hybrid renewable energy system considering the random generation of WT and photovoltaic arrays (PV). We also note that a couple of other probabilistic methods have also been adopted to handle the randomness in microgrid planning, e.g., Markovian sizing approach [14], sample average approximation method [15], and conditional value-at-risk (CVaR) [16], which have different trade-offs between modeling advantages and computational requirements. The common feature of those methods is to make use of the uncertainty information contained in rich data.…”

Section: Introductionmentioning

confidence: 99%

A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning

Cao

Wang

Zeng

2018

IEEE Trans. Power Syst.

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This paper presents a chance constrained information gap decision model for multi-period microgrid expansion planning (MMEP) considering two categories of uncertainties, namely random and non-random uncertainties. The main task of MMEP is to determine the optimal sizing, type selection, and installation time of distributed energy resources (DER) in microgrid. In the proposed formulation, information gap decision theory (IGDT) is applied to hedge against non-random uncertainties of long-term demand growth. Then, chance constraints are imposed in the operational stage to address the random uncertainties of hourly renewable energy generation and load variation. The objective of chance constrained information gap decision model is to maximize the robustness level of DER investment meanwhile satisfying a set of operational constraints with a high probability. The integration of IGDT and chance constrained program, however, makes it very challenging to compute. To address this challenge, we propose and implement a strengthened bilinear Benders decomposition method. Finally, the effectiveness of proposed planning model is verified through the numerical studies on both the simple and practical complex microgrid. Also, our new computational method demonstrates a superior solution capacity and scalability. Compared to directly using a professional mixed integer programming solver, it could reduce the computational time by orders of magnitude.

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“…A methodology to systematically formulate a hybrid system consisting the wind, solar and diesel generator as a backup resource as well as battery storage, from the preliminary design stage to the optimal operation is proposed in [13]. In Reference [14], a new approach is proposed to incorporate the uncertainties associated with RERs and load demand in sizing in the application of buildings with low to high renewable energy ratio. An optimal power generation and load management problems in off-grid hybrid electric systems with RERS is addressed in Reference [15].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Renewable Energy Resources in Hybrid Energy Systems

Reddy

2017

JGE

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This paper proposes the optimization of renewable energy resources (RERs) in the hybrid energy systems in a sustainable hybrid energy system. The behavior of renewable energy is uncertain and it is difficult for static optimization methods to optimize the uncertain non-stationary distributed energy resources in the hybrid system. A multi-objective based on the stochastic technique for optimizing total system losses and operating cost is formulated for the hybrid energy system. The proposed objective function aims to minimize the system losses and the total operating cost of RERs in different locations of the grid. In this paper, a next generation of grid connected RERs and load demand is proposed by considering the variability and uncertainty. Here, a robust stochastic approach is proposed by using the various probability distribution functions to represent the statistics of RERs. The simulation results of this paper handle the system operations under uncertainty. The proposed approach is tested on IEEE 37 node distribution system. The simulation results show the effectiveness of the proposed optimization approach in the hybrid energy system.

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Stochastic optimization of hybrid renewable energy systems using sampling average method

Cited by 72 publications

References 23 publications

Bi-objective optimization of a grid-connected decentralized energy system

Bi-objective optimization of a grid-connected decentralized energy system

A Chance Constrained Information-Gap Decision Model for Multi-Period Microgrid Planning

Optimization of Renewable Energy Resources in Hybrid Energy Systems

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