A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers

Elkazaz, Mahmoud; Sumner, Mark; Naghiyev, Eldar; Pholboon, Seksak; Davies, Ruth; Thomas, David William

doi:10.1016/j.apenergy.2020.115118

Cited by 64 publications

(71 citation statements)

References 36 publications

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“…Is it however also demonstrated that the battery power cannot be too great compared to the battery energy constraint, otherwise the battery will be drained too quickly, and the peak demand for a period of high demand will not be reduced by combining grid and batty power. This can also be seen in [16], [17]. There are some minor variations from this trend.…”

Section: Discussionmentioning

confidence: 54%

Sizing Electric Battery Storage System for Prosumer Villas

Andresen

Sale

Degefa

2020

2020 International Conference on Smart Energy Systems and Technologies (SEST)

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This paper studies the electricity consumption of 5 villas in the south of Norway and estimates the effect of utilizing batteries as a means to reduce peak load for each villa. High-resolution field data on the consumption pattern for the villas is presented. A simple battery model is utilized, and a parameter sensitivity study is performed varying the energy storage capacity and the charging/discharging power limits of the batteries. Considerable potential is found for reducing the peak load by the utilization of batteries. An appropriate balance between the energy storage capacity and the power capacity of the battery is found to be beneficial for optimization of the peak load reduction.

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Section: Discussionmentioning

confidence: 54%

Sizing Electric Battery Storage System for Prosumer Villas

Andresen

Sale

Degefa

2020

2020 International Conference on Smart Energy Systems and Technologies (SEST)

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“…Das et al (2021) propose to optimize photovoltaic generation and battery storage use in the context of hourly changing electricity prices. Elkazaz et al (2020) consider a household with local photovoltaic generation and battery storage, and propose a scheme consisting of day-ahead optimization as well as real-time control to manage the errors caused to the optimization by uncertain forecasts. Elkazaz et al (2020), Das et al (2021), Hu et al (2021) are all examples of methods that use real-time measurements as the input to an algorithm that handles the error of medium-term renewable generation forecasts, so an obvious line of further research would be to exploit nowcasting.…”

Section: Nowcasting For Vpp With Photovoltaic Generationmentioning

confidence: 99%

“…Elkazaz et al (2020) consider a household with local photovoltaic generation and battery storage, and propose a scheme consisting of day-ahead optimization as well as real-time control to manage the errors caused to the optimization by uncertain forecasts. Elkazaz et al (2020), Das et al (2021), Hu et al (2021) are all examples of methods that use real-time measurements as the input to an algorithm that handles the error of medium-term renewable generation forecasts, so an obvious line of further research would be to exploit nowcasting. Nefedov et al (2018) exploit electric vehicle batteries for short-term storage of surplus photovoltaic energy, updating plans as vehicles arrive or if they leave unexpectedly.…”

Section: Nowcasting For Vpp With Photovoltaic Generationmentioning

confidence: 99%

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

et al. 2021

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The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.

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“…However, the EMS was so general, and it was based on a net metering scheme. In some studies [26][27][28], a predictive part has been added in the EMS design to forecast the load consumption of PV generation. Lack of predictiveness is one of the limitations of the current study.…”

Section: Literature Review and Research Gapsmentioning

confidence: 99%

Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates

et al. 2021

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This paper develops new practical rule-based energy management systems (EMSs) for typical grid-connected houses with solar photovoltaic (PV) and battery by considering different rates for purchasing and selling electricity. The EMSs are developed to supply the household’s loads and reduce operating costs of the system based on different options of flat and time-of-use (ToU) rates for buying and selling electricity prices. Four different options are evaluated and compared in this study: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. The operation cost is calculated based on the electricity exchange with the main grid, the equivalent cost of PV generation, as well as the degradation cost of battery storage. The operation of the grid-connected house with rooftop solar PV and battery is evaluated for a sunny week in summer and a cloudy week in winter to investigate the proper performance for high and low generations of PV. While the developed rule-based EMS are generic and can be applied for any case studies, a grid-connected house in Australia is examined. For this purpose, real data of solar radiation, air temperature, electricity consumption, and electricity rates are used. It is found that the ToU-Flat option has the lowest operating cost for the customers.

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A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers

Cited by 64 publications

References 36 publications

Sizing Electric Battery Storage System for Prosumer Villas

Sizing Electric Battery Storage System for Prosumer Villas

Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates

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