Computational optimization techniques applied to microgrids planning: A review

Gamarra, Carlos; Guerrero, Josep M.

doi:10.1016/j.rser.2015.04.025

Cited by 332 publications

(179 citation statements)

References 112 publications

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“…Thus, the initial performance values of all sub-criteria of three micro-grid planning programs can be obtained. To eliminate the influences of dimensions and units of sub-criteria on evaluation results, we need to standardize the initial performance values via Equations (15) and (16) to obtain the normalization decision matrix E, which is listed as below: …”

Section: Standardize the Original Performance Valuesmentioning

confidence: 99%

Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory

2018

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Abstract:As useful supplements and effective support for large-scale electric power networks, micro-grid systems are the development tendency of future electric power systems. The planning performance of a micro-grid not only affects its security, reliability and economy, but also has a profound influence on the stable operation of large-scale electric power networks with the increasing penetration of micro-grids. Hence, studies related to micro-grid planning program evaluation are of great significance. This paper established a novel multi-criteria decision making (MCDM) model combining the best-worst method (BWM), the entropy weighting approach, and grey cumulative prospect theory for optimum selection of micro-grid planning programs. Firstly, an evaluation index system containing 18 sub-criteria was built from the perspectives of economy, electricity supply reliability and environmental protection. Secondly, the weights of sub-criteria were calculated integrating the subjective weights judged by the BWM and the objective weights computed by the entropy weighting method. Then, the cumulative prospect theory (CPT) combined with grey theory was employed to select the optimal micro-grid planning program. The empirical result indicates that the program with 100 kWp photovoltaic power generation unit, 200 kW wind power generation unit and 600 kWh NaS battery energy storage system is the optimal micro-grid planning program. To verify the robustness of obtained result, a sensitivity analysis related to values change of parameters under different risk preferences was conducted, and the result indicates that the selected optimal micro-grid planning program will not be influenced by various risk preferences of decision makers (DMs) and investors. The novel MCDM proposed in this paper is applicable and feasible in the micro-grid planning programs evaluation and selection.

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Section: Standardize the Original Performance Valuesmentioning

confidence: 99%

Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory

2018

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“…The expected compromise or trade-off solutions addressing conflicting objectives for satisfying multiple stakeholders, make multi-objective planning (MOP) a suitable choice for SDNs [22]. The real-world planning problem can be considered a multi-objective (MO), constrained and practically stochastic basic optimization problem [15]. The optimal DER sizing and placement will be the focus of SDN planning [23].…”

Section: Futuristic Sdn Planning: Aim and Scope Of Workmentioning

confidence: 99%

“…In addition, objectives like reliability, power quality and low (negative) environmental impact, system stability, energy efficiency and customer satisfaction can also be considered in SDN problem formulation. The SDN planning problems have been classified as follows [15,17,24,25].…”

Section: Futuristic Sdn Planning: Aim and Scope Of Workmentioning

confidence: 99%

“…Conventionally, these problems have been individually addressed in the literature. The multi-objective-based RWO problems aiming at SDN planning has to be addressed in a deeper, broader and integrated/aggregated manner, as advocated in [15]. The aggregated planning model (APM)-based primitive approach has proposed in this subsection, aiming at the implementation of RWO based SDN aggregated planning, as shown in Figure 8.…”

Section: Need For An Aggregated Planning Model For Sdnmentioning

confidence: 99%

“…Gamarra et al [15] presented a technical review of the literature on optimization techniques for planning, applied to one of the most popular SDN concepts, known as a microgrid (MG). The reviewed work serves as a guideline for innovative planning methodologies, primarily based on economic feasibility.…”

Section: Introductionmentioning

confidence: 99%

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Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective

et al. 2017

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Smart grids (SGs), as an emerging grid modernization concept, is spreading across diverse research areas for revolutionizing power systems. SGs realize new key concepts with intelligent technologies, maximizing achieved objectives and addressing critical issues that are limited in conventional grids. The SG modernization is more noticeable at the distribution grid level. Thus, the transformation of the traditional distribution network (DN) into an intelligent one, is a vital dimension of SG research. Since future DNs are expected to be interconnected in nature and operation, hence traditional planning methods and tools may no longer be applicable. In this paper, the smart distribution network (SDN) concept under the SG paradigm, has presented and reviewed from the planning perspective. Also, developments in the SDN planning process have been surveyed on the basis of SG package (SGP). The package presents a SDN planning foundation via major SG-enabling technologies (SGTF), anticipated functionalities (SGAF), new consumption models (MDC) as potential SDN candidates, associated policies and pilot projects and multi-objective planning (MOP) as a real-world optimization problem. In addition, the need for an aggregated SDN planning model has also been highlighted. The paper discusses recent notable related works, implementation activities, various issues/challenges and potential future research directions; all aiming at SDN planning.

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Deep learning and reinforcement learning approach on microgrid

Kumar¹,

Kandasamy

Ramanathan

2020

Int Trans Electr Energ Syst

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Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis-based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision-making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. K E Y W O R D S deep learning, deep reinforcement learning, microgrid, reinforcement learning 1 | INTRODUCTION Microgrid is a cluster of distributed generators (DG), stored energy system, local loads along with protecting and monitoring devices. Microgrid has its own specialty in operating under two modes of operation: autonomous and grid-connected mode, mode selection attained by the use of static transfer switch that bridges operation selection in microgrid. The presence of multiple DGs creates a distribution access problem in multipoint is transformed into a concentrated

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Computational optimization techniques applied to microgrids planning: A review

Cited by 332 publications

References 112 publications

Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory

Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory

Smart Distribution Networks: A Review of Modern Distribution Concepts from a Planning Perspective

Deep learning and reinforcement learning approach on microgrid

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