Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

Watari, Daichi; Taniguchi, Ittetsu; Goverde, Hans; Manganiello, Patrizio; Shirazi, Elham; Catthoor, Francky; Onoye, Takao

doi:10.1016/j.apenergy.2021.116671

Cited by 28 publications

(18 citation statements)

References 38 publications

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“…have been also investigated to utilize the solar energy effectively. In [13][14][15][16], the main object of these researches includes demand reduction and peak shaving by the scheduling of the battery and smart home appliances. Watari et al [14] proposed an energy management method that takes into account battery management.…”

Section: Researches On Consumer-side Energy Managementmentioning

confidence: 99%

“…In [13][14][15][16], the main object of these researches includes demand reduction and peak shaving by the scheduling of the battery and smart home appliances. Watari et al [14] proposed an energy management method that takes into account battery management. This research achieved efficient battery management and household profit maximization.…”

Section: Researches On Consumer-side Energy Managementmentioning

confidence: 99%

“…For example, Scenario 5 shows that all households use an EV aggregator four times a week, and 50 out of 100 households are Group A. In the experimental scenarios, we discriminate between Group A and Group B by the total balance of electricity demand and generation over a 90-day period, based on Equations ( 13) and (14). We set 𝐵𝑆 ℎ and 𝐵𝑆 𝑣 to 10kWh and to 40 kWh, respectively.…”

Section: Effectiveness In Reducing Wasted Energymentioning

confidence: 99%

See 2 more Smart Citations

EV Aggregation Framework for Spatiotemporal Energy Shifting to Reduce Solar Energy Waste

Kato

Watari

Taniguchi

et al. 2023

IEICE Trans. Fundamentals

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Kenshiro KATO †a) , Daichi WATARI †b) , Nonmembers, Ittetsu TANIGUCHI †c) , and Takao ONOYE †d) , Members SUMMARY Solar energy is an important energy resource for a sustainable society and is massively introduced these days. Household generally sells their excess solar energy by the reverse power flow, but the massive reverse power flow usually sacrifices the grid stability. In order to utilize renewable energy effectively and reduce solar energy waste, electric vehicles (EVs) takes an important role to fill in the spatiotemporal gap of solar energy. This paper proposes a novel EV aggregation framework for spatiotemporal shifting of solar energy without any reverse power flow. The proposed framework causes charging and discharging via an EV aggregator by intentionally changing the price, and the solar energy waste is expected to reduce by the energy trade. Simulation results show the proposed framework reduced the solar energy waste by 68%.

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Section: Researches On Consumer-side Energy Managementmentioning

confidence: 99%

Section: Researches On Consumer-side Energy Managementmentioning

confidence: 99%

Section: Effectiveness In Reducing Wasted Energymentioning

confidence: 99%

See 1 more Smart Citation

EV Aggregation Framework for Spatiotemporal Energy Shifting to Reduce Solar Energy Waste

Kato

Watari

Taniguchi

et al. 2023

IEICE Trans. Fundamentals

Self Cite

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“…Their experimental results reduced energy consumption and maintained thermal comfort. Watari et al proposed a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage [8]. Based on that work, another study proposed a thermal comfort-aware online energy management framework [9] for smart residential buildings.…”

Section: Introductionmentioning

confidence: 99%

A Thermal Comfort and Peak Power Demand Aware VRF Heating/Cooling Management Framework: Simulation and On-site Experiment

Zhao

Watari

Ozawa

et al. 2022

Journal of Information Processing

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We designed and experimentally studied an online management framework for building variable refrigerant flow (VRF) heating/cooling systems, which achieve peak shaving and thermal comfort improvement. We also developed a thermal equivalent circuit model (TECM) to describe a building's thermal behavior. The TECM was experimentally validated under different ambient temperatures, heat/cooling loads, and occupations. The temperature responses obtained from it have good agreement with observations, and the maximum deviation is below 1.9 • C. We formulated a model predictive control (MPC) problem for VRF heating/cooling system control whose objective is to minimize electric costs, reduce power demand peaks, and maximize thermal comfort. An online management framework of the VRF heating/cooling system, which was developed based on TECM, includes a monitoring system based on built-in sensors, embedded technology, and a real-time VRF system control module based on the MPC problem. We investigated the performance of the proposed framework with different operating conditions in an actual room. The VRF heating/cooling systems using this framework achieved better room temperature control and improved energy efficiency.

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“…In terms of control strategy, a model predictive control MPC controller based on random scenes combined with a nonlinear Modelica model provides a richer architectural description and captures architectural dynamics more accurately than linear models [3]. Literature [4] proposed a multitime-scale energy management framework for smart photovoltaic (PV) systems, which can calculate optimal plans for battery operation, power purchase, and application usage. A smart photovoltaic system is a local energy community that includes multiple buildings and homes equipped with photovoltaic panels and batteries.…”

Section: Introductionmentioning

confidence: 99%

Research on Cooperative Optimization Control Strategy of Multitype Energy Sources

Liu

Chen

Tan

et al. 2021

Mathematical Problems in Engineering

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With the increasing consumption of energy, the efficiency of energy utilization urgently needs to be further improved. At present, the coordinated optimization control of the integrated energy system of multiple types of cooling, heating, and power equipment is an important way to improve the comprehensive energy efficiency of the regional power grid and reduce the operating cost of the power grid. Aiming at this scenario, this paper establishes a fine energy storage model by analyzing the uncertainty of wind power output and considering the influence of low temperature and other conditions on the energy storage device in the energy storage side. On the load side, the influence of comprehensive demand response of electricity and heat on system operation is analyzed, and a combined cold, heat, and electricity supply system including renewable energy and energy storage device is established. Aiming at the optimal total cost of the integrated energy cooling and heating triple power system collaborative optimization control, the crow search algorithm is used to iteratively optimize the configuration model of the triple power system. The four models are considered in the article include groundless heat pump and energy storage, excluding joint demand response, including ground source heat pump and traditional energy storage model, excluding joint demand response, including ground source heat pump and fine energy storage model, excluding joint demand response, including ground source heat pump and fine energy storage model, taking into account the joint demand response. The simulation results show that the cooperative optimization control strategy of the combined cooling, heating, and power system with renewable energy and fine energy storage device model can enhance the system’s schedulable space, improve the comprehensive energy utilization efficiency, and have considerable economic benefits.

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Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

Cited by 28 publications

References 38 publications

EV Aggregation Framework for Spatiotemporal Energy Shifting to Reduce Solar Energy Waste

EV Aggregation Framework for Spatiotemporal Energy Shifting to Reduce Solar Energy Waste

A Thermal Comfort and Peak Power Demand Aware VRF Heating/Cooling Management Framework: Simulation and On-site Experiment

Research on Cooperative Optimization Control Strategy of Multitype Energy Sources

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