A conceptual framework for full optimal operation of a grid-connected DC microgrid

Sechilariu, Manuela; Locment, Fabrice; Santos, Leonardo Trigueiro dos

doi:10.1109/ieses.2018.8349892

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Energy management of a DC microgrid presents peculiar aspects and functions, as reported by previous authors [37][38][39]. In-line with this framework, a general control structure is illustrated in a previous study [40]. In another study [41], an optimization model with voltage network constraints is depicted, whereas semidefinite programming is exploited in a previous study [42] to deal with non-linear features.…”

Section: Introductionmentioning

confidence: 76%

DC-Microgrid Operation Planning for an Electric Vehicle Supply Infrastructure

et al. 2019

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The integration of electric vehicles (EVs) in power systems can be encouraged by charging station diffusion. These stations can perform smart charging processes, and can take advantage of the involvement of distributed generation sources in a microgrid framework. Furthermore, since photovoltaic batteries and EVs are sources based on direct current (DC), the realization of a DC microgrid structure is promising, though challenging. In this paper, a mixed-integer linear procedure for determining optimal operation planning of a DC-based electric vehicle supply infrastructure is proposed. The procedure aims at optimizing daily operational costs, based on forecast of photovoltaic production and EV exploitation. Peculiar aspects of energy storage devices and of the DC microgrid framework are accounted for through a non-linear iterative procedure. The proposed approach is applied to a test DC microgrid on different operation days and its effectiveness is compared to non-linear formulation solved by means of a genetic algorithm.

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Section: Introductionmentioning

confidence: 76%

DC-Microgrid Operation Planning for an Electric Vehicle Supply Infrastructure

et al. 2019

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“…Thereby, DL methods outperform other alternative ML approaches [48]. Moreover, the performance of DL methods are comparable and, in some cases, superior to engineering methods such as expert-based models [49], [50], fuzzy logic [51], mixed-integer linear programming [52]. As a result, DL methods have attracted significant attention in recent studies in building's energy management modelling.…”

Section: A Forecasting Methodsmentioning

confidence: 99%

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

et al. 2021

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Over 33% of final energy consumption is used in buildings which leads to nearly 40% of total direct and indirect CO 2 emissions in the world. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method considers several factors including a two-step electricity price, uncertainty in climatic factors, availability of renewable energy resources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters. This novel method analyzes and continuously learns from data patterns based on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management. The main objective of the proposed method is to minimize the reliance on the grid and electricity cost, especially during the peak days. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The results also show that the method can supply 304 days (83.2%) of a year without reliance on energy grids, decreasing 87.2% in energy demand on one hand and exporting annually 7777 kWh to the grid on the other hand. In addition, the rescheduling framework decreased the imported electricity cost with the higher electricity tariff by 98 %. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid from 6709 to 858 kWh (84.3%).

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“…Renewable energy sources such as photovoltaic (PV) sources can directly produce direct current (DC) power to supply most electric appliances, and a DC microgrid [4,5] can increase the efficiency of the power supply by decreasing the number of AC/DC converters. A hybrid microgrid can satisfy different electric appliances, but it is complex to control and to keep power balanced [6,7].…”

Section: Introductionmentioning

confidence: 99%

DC Microgrid System Modeling and Simulation Based on a Specific Algorithm for Grid-Connected and Islanded Modes with Real-Time Demand-Side Management Optimization

2020

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This paper presents an algorithm considering both power control and power management for a full direct current (DC) microgrid, which combines grid-connected and islanded operational modes, with real-time demand-side management optimization. The full microgrid is a hybrid dynamic system model consisting of two interacting parts: continuous-time dynamics and discrete-event dynamics. Such a full microgrid consists of photovoltaic sources, a DC load, battery storage systems, supercapacitor storage, a diesel generator, and a public grid connection, all connected on a DC common bus. This full microgrid is more reliable than a microgrid with only renewable sources or with only traditional energy sources, considering the power constraints imposed by the public grid as well as the sluggish dynamic of the diesel generator, self-discharging characteristic of the supercapacitor, and load shedding optimization. Meanwhile, this algorithm can automatically switch between grid-connected and islanded operational modes to optimize the power of the load shedding, take advantage of renewable energy, and keep the power balance in the full DC microgrid. The results under MATLAB/Simulink verify that the real-time control algorithm can maintain the power balance in real-time for the whole day and satisfy the power management strategy.

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A conceptual framework for full optimal operation of a grid-connected DC microgrid

Cited by 5 publications

References 17 publications

DC-Microgrid Operation Planning for an Electric Vehicle Supply Infrastructure

DC-Microgrid Operation Planning for an Electric Vehicle Supply Infrastructure

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

DC Microgrid System Modeling and Simulation Based on a Specific Algorithm for Grid-Connected and Islanded Modes with Real-Time Demand-Side Management Optimization

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