• Energy Research

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.

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.

Abstract With the development of home area network, residents have the opportunity to schedule their power usage at the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, an energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented that considers both energy consumption and generation simultaneously. The proposed model arranges the household electrical and thermal appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. In this model, the home gateway receives the electricity price information as well as the resident desired options in order to efficiently schedule the appliances and shave the peak as well. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, PV panel and a battery over a 24-h period.

Abstract With the development of home area network, residents have the opportunity to schedule their power usage at the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, an energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented that considers both energy consumption and generation simultaneously. The proposed model arranges the household electrical and thermal appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. In this model, the home gateway receives the electricity price information as well as the resident desired options in order to efficiently schedule the appliances and shave the peak as well. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, PV panel and a battery over a 24-h period.

Nowadays, due to the increasing number of disasters, improving distribution system resiliency is a new challenging issue for researchers. One of the main methods for improving the resiliency in distribution systems is to supply critical loads after disasters during the power outage and before system restorations. In this paper, a “Sustainable and resilient smart house” is introduced for the first time by using plug-in hybrid electric vehicles (PHEVs). PHEVs have the ability to use their fuel for generating electricity in emergency situations as the Vehicle to Grid (V2G) scheme. This ability, besides smart house control management, provides an opportunity for distribution system operators to use their extra energy for supplying a critical load in the system. The proposed control strategy in this paper is dedicated to a short duration power outage, which includes a large percent of the events. Then, improvement of the resiliency of distribution systems is investigated through supplying smart residential customers and injecting extra power to the main grid. A novel formulation is proposed for increasing the injected power of the smart house to the main grid using PHEVs. The effectiveness of the proposed method in increasing power injection during power outages is shown in simulation results.

Nowadays, due to the increasing number of disasters, improving distribution system resiliency is a new challenging issue for researchers. One of the main methods for improving the resiliency in distribution systems is to supply critical loads after disasters during the power outage and before system restorations. In this paper, a “Sustainable and resilient smart house” is introduced for the first time by using plug-in hybrid electric vehicles (PHEVs). PHEVs have the ability to use their fuel for generating electricity in emergency situations as the Vehicle to Grid (V2G) scheme. This ability, besides smart house control management, provides an opportunity for distribution system operators to use their extra energy for supplying a critical load in the system. The proposed control strategy in this paper is dedicated to a short duration power outage, which includes a large percent of the events. Then, improvement of the resiliency of distribution systems is investigated through supplying smart residential customers and injecting extra power to the main grid. A novel formulation is proposed for increasing the injected power of the smart house to the main grid using PHEVs. The effectiveness of the proposed method in increasing power injection during power outages is shown in simulation results.

Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential for efficient energy management. We have developed a new electricity price forecasting model that provides consistently accurate forecasts. The base prediction model decomposes the time series using wavelet transform and then predicts it by Long Short-Term Memory. Previous studies using this model have always decomposed time series in the same way without changing the mother wavelet. However, this makes it difficult to respond to changes in time series that vary daily or seasonally. Therefore, we periodically switch the mother wavelet, i.e., flexibly change the time series decomposition method, to achieve stable and highly accurate electricity price forecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to prediction with a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method can consistently provide highly accurate forecasts.