• Energy Research

This paper proposes MIP (Mixed-Integer Programming)-based power distribution optimization method for decentralized energy network with electric vehicles (EVs). Decentralized energy network is a new grid systems toward independence from existing power grid, and renewable energy is main energy source for the decentralized energy network. EVs are expected to take an important role of future mobility and energy network. Because the latest EV usually include large battery for long cruising distance, now EVs can be regarded as “moving battery.” Proposed method utilizes both battery in house and EV's battery to minimize total purchased energy and wasted energy. This method brings an opportunity to charge the surplus energy at house to EV's battery, and the total purchased energy may be reduced. Experimental results demonstrated the interconnection between the energy network with rich connectivity and EV reduced the purchased energy by more than half.

Iwabuchi K., Watari D., Zhao D., et al. Enhancing grid stability in PV systems: A novel ramp rate control method utilizing PV cooling technology. Applied Energy 378, 124737 (2025); https://doi.org/10.1016/j.apenergy.2024.124737. ; Rapid fluctuations in solar irradiation lead to significant variability in PV power output. Traditional ramp rate control methods use battery energy storage systems to smooth power outputs and provide a more consistent supply to the grid. However, these methods require high initial costs and substantial maintenance. In this study, we propose a novel method for controlling PV power output ramp rates using cooling technology, which is essential to stabilize grid operations and ancillary services. The proposed method adjusts power generation efficiency in real-time by controlling PV panel temperature, leveraging their thermoelectric properties. The effectiveness of our method was validated by simulation based on real-world data, which showed reductions in mean and maximum ramp rates of 43.5% and 76.2%, respectively, compared to traditional battery storage solutions. Notably, these improvements were achieved with a cooling unit having a coefficient of performance of less than 10 and a minimal battery capacity of 20 kWh, highlighting the efficiency of the method and its potential to significantly lower system costs and environmental impacts compared to traditional control strategies.

Abstract We propose 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. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.

It is not easy to provide energy supply based on renewable energy enough to satisfy energy demand anytime and anywhere because renewable energy amounts depends on geographical conditions and the time of day. This paper proposes a novel autonomous decentralized mechanism of energy interchanges between distributed batteries on the basis of the diffusion equation and MCMC (Markov chain Monte Carlo) for realizing energy supply appropriately for energy demand. Experimental results show the proposed mechanism effectively works under several situations.

Kaneko N., Okazawa K., Zhao D., et al. Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems. Applied Energy 367, 123379 (2024); https://doi.org/10.1016/j.apenergy.2024.123379. ; Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room's thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the ...

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

SUMMARYThe renewal of conventional energy systems is important countermeasures against global warming effects and natural hazards. A self‐sustainable decentralized energy system is one of the promising solutions for future sustainable and resilient societies. In this paper, a mathematical programming model is formulated and design and utilization of the overall energy network is optimized based on the model, where stationary batteries are equipped. Through some numerical simulation results, the effectiveness and the potential, for example, for clarifying the effect of the batteries, of the proposed model are investigated.