A comprehensive review on uncertainty modeling techniques in power system studies

Aien, Morteza; Hajebrahimi, Ali; Fotuhi‐Firuzabad, Mahmud

doi:10.1016/j.rser.2015.12.070

Cited by 349 publications

(168 citation statements)

References 95 publications

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“…Uncertainty managing is one of the main difficulties decision makers have to deal with [46]. As a result, various methods have been employed in order to manage the uncertainty of the aforementioned parameters in Section 3 in microgrid energy management [46,[54][55][56][57]. In this section, a review of all existing uncertainty modeling techniques used by an EMS are presented which are classified broadly into four categories as shown in Figure 2.…”

Section: Uncertainty Modelingmentioning

confidence: 99%

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

et al. 2019

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Aggregation of distributed generations (DGs) along with energy storage systems (ESSs) and controllable loads near power consumers has led to the concept of microgrids. However, the uncertain nature of renewable energy sources such as wind and photovoltaic generations, market prices and loads has led to difficulties in ensuring power quality and in balancing generation and consumption. To tackle these problems, microgrids should be managed by an energy management system (EMS) that facilitates the minimization of operational costs, emissions and peak loads while satisfying the microgrid technical constraints. Over the past years, microgrids' EMS have been studied from different perspectives and have recently attracted considerable attention of researchers. To this end, in this paper a classification and a survey of EMSs has been carried out from a new point of view. EMSs have been classified into four categories based on the kind of the reserve system being used, including non-renewable, ESS, demand-side management (DSM) and hybrid systems. Moreover, using recent literature, EMSs have been reviewed in terms of uncertainty modeling techniques, objective functions (OFs) and constraints, optimization techniques, and simulation and experimental results presented in the literature.

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Section: Uncertainty Modelingmentioning

confidence: 99%

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

et al. 2019

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“…Studies and research on power systems need to consider various uncertainties. Regarding the tendency of most power systems to be privately owned, as well as taking into account the constant changes in load and generation, the uncertainty issue in controlling and exploiting power systems is becoming a serious issue [33]. Uncertainties reflect the lack of accurate information on the values of parameters, system components, and measurements.…”

Section: Uncertainties In the Proposed Modelmentioning

confidence: 99%

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

et al. 2019

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In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.The major share of the energy production in microgrids usually comes from renewable energy sources such as photovoltaic panels (PV) or wind turbines (WT). The intermittent nature of such units can cause fluctuation and power imbalance in the system. Hence, energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3-6]. State-of-the-ArtResearch on all aspects of microgrids has become very popular in the recent past. In this review we will concentrate on optimization objectives in microgrids only since this is the topic of this paper.Reference [3] presents a review of existing optimization objectives, tools, and solution approaches used in microgrid energy management. In [6], a summary of various uncertainty quantification methods along with a comparative analysis on utilized communication technologies are presented. An energy management solution based on a Bayesian optimization algorithm (BOA) is proposed by [7] in which the optimization problem is formulated without a closed-form objective function expression, and solved using a BOA-based data-driven framework. In [8], a two-layer predictive energy management system (EMS) for microgrids was proposed. The upper layer reduces the total operational cost and the lower layer try to remove the forecast fluctuations. In [9], an optimal scheduling of a standalone microgrid under system uncertainties is proposed. This model uses a dynamic programming method to solve a single-objective optimization problem to reduce the operation and emission costs.Flexibility and cooperative behavior of multi-agent systems (MASs) make them appropriate candidates for ener...

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“…To solve system uncertainty, many existing mathematical models have been developed in previous literature; for example, the expected value model, fuzzy programming and chance constrained programming, and so forth [15][16][17]. Compared with other models, chance constrained programming is more flexible, and it can coherently consider the uncertainty variables in the objective function and the constraints.…”

Section: Introductionmentioning

confidence: 99%

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

Zhang

Gong

et al. 2018

Applied Sciences

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Abstract:With the rapid growth of electricity demands, many traditional distributed networks cannot cover their peak demands, especially in the evening. Additionally, with the interconnection of distributed electrical and thermal grids, system operational flexibility and energy efficiency can be affected as well. Therefore, by adding a portable energy system and a heat storage tank to the traditional distributed system, this paper proposes a newly defined distributed network to deal with the aforementioned problems. Simulation results show that by adding a portable energy system, fossil fuel energy consumption and daily operation cost can be reduced by 8% and 28.29%, respectively. Moreover, system peak load regulating capacity can be significantly improved. However, by introducing the portable energy system to the grid, system uncertainty can be increased to some extent. Therefore, chance constrained programming is proposed to control the system while considering system uncertainty. By applying Particle Swarm Optimization-Monte Carlo to solve the chance constrained programming, results show that power system economy and uncertainty can be compromised by selecting appropriate confidence levels α and β. It is also reported that by installing an extra heat storage tank, combined heat and power energy efficiency can be significantly improved and the installation capacity of the battery can be reduced.

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A comprehensive review on uncertainty modeling techniques in power system studies

Cited by 349 publications

References 95 publications

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

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