• Energy Research

Electric Vehicles (EVs) are a rapidly growing technology which can lower greenhouse-gas emissions in the transport and energy sectors. The EV batteries can discharge the stored energy back to grid, also known as Vehicle-to-Grid (V2G) which can support the integration of variable distributed renewable generation. Previous research identified financial barriers to the implementation of V2G, however, recent advancements in battery technology present new opportunities to make V2G technology viable. Using the current and predicted EV technology trends, this paper evaluates the annual operation and benefits of EVs and V2G in a microgrid environment and demonstrates different modes of operation. Guided by the gaps identified in the literature, one of the main contributions of this research is to uncover the impact of EV charging scenarios on the V2G operations. Furthermore, the research reveals the interactions between V2G and variable renewable generation coupled with utility scale battery over yearlong simulations to assess seasonal characteristics of V2G operations, which was mostly unexplored to date. Simulation results indicate that the operation of V2G in an optimised microgrid environment improves the economic operation of the system and reduces the levelized cost of electricity by up to 5.7%. Additionally, V2G provides more benefit to grids with higher solar generation proportion The results suggest that the latest advancements in EV technology have improved the economic viability of V2G as well as its potential to improve grid efficiency through providing additional storage capacity and peak demand management.

Electric Vehicles (EVs) are a rapidly growing technology which can lower greenhouse-gas emissions in the transport and energy sectors. The EV batteries can discharge the stored energy back to grid, also known as Vehicle-to-Grid (V2G) which can support the integration of variable distributed renewable generation. Previous research identified financial barriers to the implementation of V2G, however, recent advancements in battery technology present new opportunities to make V2G technology viable. Using the current and predicted EV technology trends, this paper evaluates the annual operation and benefits of EVs and V2G in a microgrid environment and demonstrates different modes of operation. Guided by the gaps identified in the literature, one of the main contributions of this research is to uncover the impact of EV charging scenarios on the V2G operations. Furthermore, the research reveals the interactions between V2G and variable renewable generation coupled with utility scale battery over yearlong simulations to assess seasonal characteristics of V2G operations, which was mostly unexplored to date. Simulation results indicate that the operation of V2G in an optimised microgrid environment improves the economic operation of the system and reduces the levelized cost of electricity by up to 5.7%. Additionally, V2G provides more benefit to grids with higher solar generation proportion The results suggest that the latest advancements in EV technology have improved the economic viability of V2G as well as its potential to improve grid efficiency through providing additional storage capacity and peak demand management.

Abstract The emergence of smart grid technologies and applications has meant there is increasing interest in utilising smart meters. Smart meter penetration has significantly increased over the last decade and they are becoming more widespread globally. Companies such as Google, Nest, Intel, General Electric and Amazon are amongst those companies which have been developing end use applications such as home and battery energy management systems which leverage smart meter data. In addition, utilities and networks are becoming more aware of the potential benefits of using household smart meter data in demand side management strategies such as energy efficiency and demand response. Motivated by this fact, the amount of research in this area has grown considerably in recent years. This paper reviews the most recent methods and techniques for using smart meter data such as forecasting, clustering, classification and optimization. The study covers various applications such as Home and Battery Energy Management Systems and demand response strategies enabled by the analysis of smart meter data. From a comprehensive review of the literature, it was observed that there are remarkable discrepancies between the studies, which make in-depth comparison and analysis challenging. Data analysis and reporting guidelines are suggested for studies which use smart meter data. These guidelines could provide a consistent and common framework which could enhance future research.

Abstract The emergence of smart grid technologies and applications has meant there is increasing interest in utilising smart meters. Smart meter penetration has significantly increased over the last decade and they are becoming more widespread globally. Companies such as Google, Nest, Intel, General Electric and Amazon are amongst those companies which have been developing end use applications such as home and battery energy management systems which leverage smart meter data. In addition, utilities and networks are becoming more aware of the potential benefits of using household smart meter data in demand side management strategies such as energy efficiency and demand response. Motivated by this fact, the amount of research in this area has grown considerably in recent years. This paper reviews the most recent methods and techniques for using smart meter data such as forecasting, clustering, classification and optimization. The study covers various applications such as Home and Battery Energy Management Systems and demand response strategies enabled by the analysis of smart meter data. From a comprehensive review of the literature, it was observed that there are remarkable discrepancies between the studies, which make in-depth comparison and analysis challenging. Data analysis and reporting guidelines are suggested for studies which use smart meter data. These guidelines could provide a consistent and common framework which could enhance future research.

Abstract Electricity load forecasting is an important tool which can be utilized to enable effective control of commercial building electricity loads. Accurate forecasts of commercial building electricity loads can bring significant environmental and economic benefits by reducing electricity use and peak demand and the corresponding GHG emissions. This paper presents a review of different electricity load forecasting models with a particular focus on regression models, discussing different applications, most commonly used regression variables and methods to improve the performance and accuracy of the models. A comparison between the models is then presented for forecasting day ahead hourly electricity loads using real building and Campus data obtained from the Kensington Campus and Tyree Energy Technologies Building (TETB) at the University of New South Wales (UNSW). The results reveal that Artificial Neural Networks with Bayesian Regulation Backpropagation have the best overall root mean squared and mean absolute percentage error performance and almost all the models performed better predicting the overall Campus load than the single building load. The models were also tested on forecasting daily peak electricity demand. For each model, the obtained error for daily peak demand forecasts was higher than the average day ahead hourly forecasts. The regression models which were the main focus of the study performed fairly well in comparison to other more advanced machine learning models.

Abstract Electricity load forecasting is an important tool which can be utilized to enable effective control of commercial building electricity loads. Accurate forecasts of commercial building electricity loads can bring significant environmental and economic benefits by reducing electricity use and peak demand and the corresponding GHG emissions. This paper presents a review of different electricity load forecasting models with a particular focus on regression models, discussing different applications, most commonly used regression variables and methods to improve the performance and accuracy of the models. A comparison between the models is then presented for forecasting day ahead hourly electricity loads using real building and Campus data obtained from the Kensington Campus and Tyree Energy Technologies Building (TETB) at the University of New South Wales (UNSW). The results reveal that Artificial Neural Networks with Bayesian Regulation Backpropagation have the best overall root mean squared and mean absolute percentage error performance and almost all the models performed better predicting the overall Campus load than the single building load. The models were also tested on forecasting daily peak electricity demand. For each model, the obtained error for daily peak demand forecasts was higher than the average day ahead hourly forecasts. The regression models which were the main focus of the study performed fairly well in comparison to other more advanced machine learning models.