Optimal power management for nanogrids based on technical information of electric appliances

Lee, Sangkeum; Lee, Jun-Ho; Jung, Heedae; Cho, Jintae; Hong, Jihyung; Lee, Sang‐Min; Har, Dongsoo

doi:10.1016/j.enbuild.2019.03.026

Cited by 29 publications

(36 citation statements)

References 24 publications

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“…Arun and Selvan [24] propose a real-time load scheduling heuristic algorithm aimed at reducing the electricity bill and keep the total consumption under maximum demand limit considering the operating dynamics of a large number of controllable and non-controllable loads. Similarly, Lee et al [25] present a hidden Markov model for the scheduling of shiftable loads in households equipped with storage systems. In addition, Ye et al [26] address the real-time DSM of a microgrid equipped with renewable energy sources and storage systems, showing that a game theoretical energy scheduling enabled by an advanced communication networks is able to smooth peak-to-average ratio (PAR) of power usage.…”

Section: Literature Reviewmentioning

confidence: 99%

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Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Carli

Dotoli

Jantzen

et al. 2020

Energy

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This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under timevarying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master's office equipped with a heat pump), PV production (60kWp), and the BESS (237kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 hour resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach.

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Section: Literature Reviewmentioning

confidence: 99%

“…3). For this reason, in (25) the objective function is defined as is the sum of unsigned flows from/to main grid.…”

Section: Online Optimization Problemmentioning

confidence: 99%

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Carli

Dotoli

Jantzen

et al. 2020

Energy

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show abstract

“…In the US, HVAC accounts for 1/3 of the total power consumption of electric appliances [4]. Due to the HVAC's high portion proportion of power consumption, other high-power electric appliances often cannot be used at the same time with as HVAC, even when they are nonshiftable appliances [5]. Therefore, the efficient operation of HVAC is critical for nanogrid power management.…”

Section: Introductionmentioning

confidence: 99%

“…The instantaneous level of power consumption of electric appliances is measured by the central controller of the nanogrid. In [5], the power management of the DC nanogrid optimized for the given time interval is presented, dealing with shiftable appliances, nonshiftable appliances, HVAC, and the ESS. The objective of the power management is to simultaneously optimize the total power consumption and a function of delays.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, more comprehensive operation of nanogrids is investigated, including EV charging. The most significant difference of this work compared to that in [5] is the use of the LSTM network as the central controller. Unlike the power management in [5], the proposed power management controlled by the LSTM network allows preadjusted (proactive) operation of HVAC to meet future operating conditions of the nanogrid.…”

Section: Introductionmentioning

confidence: 99%

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Power Management by LSTM Network for Nanogrids

et al. 2020

Self Cite

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Nanogrids can be considered smart grids that are implemented for small-scale buildings, houses, and apartments. A typical power management framework for nanogrids determines the scheduling of operations of electric appliances for each time interval with objectives related to total power consumption and total delay due to scheduling. Such a framework of power management has limitations in accommodating future operating conditions of nanogrids. Taking future outdoor temperature as a future operating condition, a proactive power management for nanogrids is presented in this paper. The goal of proactive power management for nanogrids is to achieve the proper level of indoor temperature in a cost-efficient way, sooner rather than later, by taking into account future outdoor temperature. To achieve this goal, a long short-term memory (LSTM) network is used as the controller. Simulations have been performed to verify the performance of the proposed power management. The results of the simulations demonstrate that living comfort measured in terms of room temperature is enhanced while the overall electricity cost is reduced, mainly due to the ability of the LSTM network to predict the trend of outdoor temperature.

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Development of deep learning‐based equipment heat load detection for energy demand estimation and investigation of the impact of illumination

Wei

Calautit

2020

Int J Energy Res

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As the use of equipment in office buildings increases, accurate equipment usage detection is valuable for the reduction of energy consumption and carbon emission. Using the collected equipment usage information, building energy management system (BEMS) can automatically adjust the operation of heating, ventilation, and airconditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real time. Previous studies highlighted that the use of conventional control strategies in office buildings such as "static" operation schedules could cause large energy waste in particular during unoccupied hours. Common equipment load detection techniques, such as power meters and survey, are unable to provide comprehensive (location, count and heat emission) and real-time equipment information necessary for demand-driven strategies. Based on a deep learning-based equipment load detection approach which employs artificial intelligence-enabled cameras, the detection performance of the proposed approach is likely going to be affected by environmental conditions, such as the positions of cameras and lighting level, within the monitored spaces. Improper detection conditions can result in incorrect detection and lead to inaccurate building energy demand estimation. This paper aims to investigate the influences of illumination conditions on the detection accuracy of the vision-based equipment load detection approach. The work will be using AI cameras to detect equipment information under different lighting levels, employing deep learning method to analyse and generate the real-time equipment usage profiles for offices which can be inputted to the BEMS to increase the efficiency of HVAC systems. The results showed that as compared with the conventionally-scheduled HVAC systems, the HVAC system with the use of equipment usage profiles conducted by the proposed approach can achieve up to 15% reduction of energy consumption. The finding indicates that adequate illumination level contributes to the decrease of building energy demand by achieving an effective deep learning approach.

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Optimal power management for nanogrids based on technical information of electric appliances

Cited by 29 publications

References 24 publications

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Power Management by LSTM Network for Nanogrids

Development of deep learning‐based equipment heat load detection for energy demand estimation and investigation of the impact of illumination

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