Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Fujimoto, Yu; Ishii, Hideo; Hayashi, Yasuhiro

doi:10.1002/tee.23210

Cited by 21 publications

(5 citation statements)

References 198 publications

(201 reference statements)

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“…Moreover, these systems must be autonomously or remotely controlled. Several methods for power grids optimization have been proposed, such as the simulation of the power grid system characteristics of an actual city and the simulation of an energy management system at the city scale [9,10,29].…”

Section: Data Use For Smarter Power Grid Systemsmentioning

confidence: 99%

“…A detailed spatiotemporal balance between supply and demand is required to achieve regional selfsufficiency effectively. In particular, the concept of energy management, such as charging appropriately installed storage batteries and controllable EVs using surplus PV power and discharging them in periods and regions with high power demand, will have an important role [9,10]. Thus, the ability to predict the spatiotemporally uneven distribution of PV surplus power and power demand in a city is important for efficiently realizing energy management to improve local self-sufficiency.…”

Section: Introductionmentioning

confidence: 99%

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Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities

et al. 2021

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The use of distributed energy resources (DERs) in a city contributes to the net zero CO2 of a city. However, the spatially uneven distribution of power demand and surplus electricity causes congestion in the grid system, making wide‐area operation difficult. The concept of local energy self‐sufficiency via energy management, in which batteries or electric vehicles are charged using power generated by DERs and discharged to neighbouring consumers, is expected to be a way to avoid grid conjunction while maximizing the use of DERs. For efficient local energy self‐sufficiency, it is necessary to identify where and when future power surpluses and shortages will occur within a city and optimize battery operation according to demand. Forecasts that focus only on representative points of a city may be less reproducible in diversity in the power demand transition for individual consumers in local parts of cities. Electricity smart meters that monitor power demand every 30 min from each consumer are expected to help predict the spatiotemporal distribution of power demand to achieve efficient local energy self‐sufficiency. The significance of reflecting regional characteristics in forecasting spatiotemporal distribution of power demand is demonstrated using actual data obtained by smart meters installed in Japanese cities. The results suggest that the forecast approach, which considers the daily periodicity of power demand and weather conditions, obtains high prediction accuracy in predicting power demand in meshed local areas in the city and derives results precisely reproducing the spatiotemporal behaviours of power demand.

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Section: Data Use For Smarter Power Grid Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities

et al. 2021

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“…While owners and workers of smart buildings require a variety of solutions such as ZEB, Wellness, BCP, etc., current building system architecture is facing its limits. Moreover, other industrial systems, for example smart city platforms (8) , smart grids (9) and other smart facilities systems (10) , are becoming connected to smart building systems (11) . It also complicates implementation in current architecture of building systems.…”

Section: Introductionmentioning

confidence: 99%

SUSTIE Core Engine: Efficient IoT Platform for Smart Facility Solutions with Gateway, Spatiotemporal Database, and Simulation Interface

et al. 2023

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The SUSTIE test facility was completed in 2020, and it facilitates to the development of energy-conservation technologies while contributing to the realization of more comfortable and energy-efficient indoor environments. The facility achieved the top rank of energy performance certification "Building-Housing Energy-efficiency Labeling System (BELS)" and wellness-office certification "Comprehensive Assessment System for Built Environment Efficiency-Wellness Office (CASBEE-WO)" in Japan.To accelerate research and evaluation, we propose an efficient Internet-of-things (IoT) platform called the SUSTIE Core Engine. The platform gathers data in real time, stores both design data and time-series data with coordinates, and act as simulation interface to satisfy three requirements of an IoT platform for smart facilities: an architecture that allows adding / removing functions without stopping operation, data management features for time-series / coordinates data, and common interface for simulations / controls. We implement the platform which covers the requirements to on-site system in the test facility, and evaluate the gateway, spatiotemporal database, and simulation interface. Our platform showed that it satisfies the three functional requirements of IoT platforms. Furthermore, it was shown that the database can obtain sufficient demonstrated for practical use.

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“…Local energy self-sufficiency is achieved not only by installing DERs in the city, but also by providing appropriate controls to levelise the uneven demand distribution among several areas within the city [8,9]. In particular, short-term forecasts of behind-the-metre pure demand and generation, which cannot be measured directly, are essential for efficient local energy self-sufficiency, as they provide dominant information for optimising controllable DERs and power demand.…”

Section: Introduction 1| Motivationmentioning

confidence: 99%

Forecast of area‐scale behaviours of behind‐the‐metre solar power and load based on smart‐metering net demand data

et al. 2023

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Local energy self‐sufficiency, in which the supply and demand of electricity are controlled such that the generated power from distributed energy resources (DERs) is consumed locally based on a power supply‐and‐demand forecast, mitigates the burden on the power system and contributes to the efficient use of DERs in smart cities. However, widely available smart metres cannot measure behind‐the‐metre pure demand and generation from prosumers. Pure demand and generation forecasts without additional metering contribute to advanced supply‐and‐demand control in smart cities, including demand response. This study proposes a method of forecasting spatio‐temporal behaviours of behind‐the‐metre pure demand and generation by focussing on the information of net demand distribution observable from the smart metres; the proposed method initially predicts the spatio‐temporal net demand distribution with a combined forecaster based on the persistence and non‐parametric regression models, and then separately estimates the behind‐the‐metre pure demand and generation by using demand forecast result of neighbouring pure‐consumers extracted by considering the area‐scale behaviours of the smart metering data. The simulation results demonstrate that the proposed method provides accuracy comparable to forecasts conducted by directly measuring pure demand and generation, without requiring the installation of additional metres.

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Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Cited by 21 publications

References 198 publications

Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities

Spatial demand forecasting based on smart meter data for improving local energy self‐sufficiency in smart cities

SUSTIE Core Engine: Efficient IoT Platform for Smart Facility Solutions with Gateway, Spatiotemporal Database, and Simulation Interface

Forecast of area‐scale behaviours of behind‐the‐metre solar power and load based on smart‐metering net demand data

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