A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids

Khan, Asad; Naeem, Muhammad; Iqbal, Muhammad; Qaisar, Saad; Anpalagan, Alagan

doi:10.1016/j.rser.2015.12.259

Cited by 203 publications

(72 citation statements)

References 170 publications

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“…Microgrid Operational Mode DR ESS Islanded Grid-Connected [6]      [7]      [8]    [9]    [10]     [11]    [12]    [13]    …”

Section: Flexible Resources Optimization Techniquesmentioning

confidence: 99%

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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: Flexible Resources Optimization Techniquesmentioning

confidence: 99%

“…EMS for a microgrid represent relatively new and popular topics that attracted lots of attention, recently. Existing review papers in literature have investigated microgrid EMSs from a different aspects, as summarized in Table 1 [6][7][8][9][10][11][12][13]. The contributions of this review paper are:…”

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confidence: 99%

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

et al. 2019

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“…The crucial and essential issue on MG researches is how to use renewable energy resources considering efficiency and reliability, which has also increasingly become a promising research interest worldwide in recent years . Nevertheless, the widespread application of MGs is high cost at present, which urges us to make innovations and improvements for the MG economy enhancement .…”

Section: Introductionmentioning

confidence: 99%

“…10 The crucial and essential issue on MG researches is how to use renewable energy resources considering efficiency and reliability, which has also increasingly become a promising research interest worldwide in recent years. [11][12][13] Nevertheless, the widespread application of MGs is high cost at present, 14 which urges us to make innovations and improvements for the MG economy enhancement. 15 Taking different batteries into account, Qiu et al 16 presented an interconnection topology and a reinforcement learning-based algorithm to make coordinative optimization for multiple energy storage systems (ESSs) in an MG.…”

Section: Introductionmentioning

confidence: 99%

Multi-timescale coordinated optimization of hybrid three-phase/single-phase multimicrogrids

Zhang

Liang

et al. 2017

Int Trans Electr Energ Syst

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Summary With the increasing amount and type of connected microgrids in the near‐term future power networks, how to optimize the operation of multimicrogrids efficiently and reliably has become essential for taking full advantage of the complex systems. In this paper, a multitimescale coordinated optimization strategy for hybrid three‐phase/single‐phase multimicrogrids is proposed. The multitimescale strategy is implemented for day‐ahead economic optimization on a long‐time scale and real‐time tracking optimization on a short‐time scale. With the consideration of the economy of microgrids and three‐phase unbalance constraints in multimicrogrids, a strategy of collecting‐distributing fuzzy modified adaptive particle swarm economic optimization is introduced. In this day‐ahead strategy, a bilayer rolling optimizing structure is designed, where the lower layer conducts economic optimization at all time slots and the upper layer balances the tie‐line power at each time slot based on the results obtained from the lower layer. On this basis, by taking a day‐ahead optimized tie‐line power as baseline values, the real‐time power of energy storage systems is obtained by adopting an improved nondominated sorting genetic algorithm, which achieves distributed real‐time optimization for the multimicrogrid. Simulation results verify that the proposed strategy is feasible.

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“…[1,2]. Energy management approaches are complex tasks which require dynamic assessment of current and future operations while considering time-varying energy consumption, production, and prices [3][4][5]. Meanwhile, by treating EV charging requests as postponable electrical loads under some appropriate demand-side management (DSM) strategies, the energy management of microgrids can be more flexible and cost-effective [6,7].…”

Section: Introductionmentioning

confidence: 99%

Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach

Huang

et al. 2016

Energies

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Abstract:A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user's demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD) technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC) approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times.

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A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids

Cited by 203 publications

References 170 publications

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

A Survey on Microgrid Energy Management Considering Flexible Energy Sources

Multi-timescale coordinated optimization of hybrid three-phase/single-phase multimicrogrids

Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach

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