Mitigation from a cross-sectoral perspective

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

A review of data-driven building energy consumption prediction studies

Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.

Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

Large-scale deployment of electric vehicles (EVs) is anticipated in the foreseeable future. Heavy intermittent charging load of EVs will create bottlenecks in supplying capacity and expose power system to severe security risks. In this paper, we propose an intelligent method to control EV charging loads in response to time-of-use (TOU) price in a regulated market. First, an optimized charging model is formulated to minimize the charging cost. Then, a heuristic method is implemented to minimize the charging cost considering the relation between the acceptable charging power of EV battery and the state of charge (SOC). Finally, the charging cost and energy demand in different time intervals are compared for both typical charging pattern and optimized charging pattern. Results show that the optimized charging pattern has great benefit in reducing cost and flatting the load curve if the peak and valley time periods are partitioned appropriately.

An Optimized EV Charging Model Considering TOU Price and SOC Curve

Roadway-powered electric vehicles (RPEVs) are attractive candidates for future transportation because they do not rely on large and heavy batteries but directly and efficiently get power while moving along a road. The inductive power transfer systems (IPTSs) that have been widely used for the wireless powering of RPEVs are reviewed in this paper. The development history of the IPTS is tracked from the origin of the RPEV in the 1890s to the recent RPEV. Throughout its 100-year history, the size, weight, efficiency, air gap, lateral tolerance, electromagnetic force, and cost of the IPTS have been substantially improved, and now RPEVs are becoming more widely commercialized. Important milestones of the developments of the IPTS and RPEVs are summarized in this paper, focusing on recent developments of on-line electric vehicles that were first commercialized in 2013.

Advances in Wireless Power Transfer Systems for Roadway-Powered Electric Vehicles

Cooperative home energy management using batteries for a photovoltaic system considering the diversity of households

The Japanese government has set a power sector goal for photovoltaic (PV) power usage to reach 53 million kW by 2030. To achieve the large-scale introduction of PV, a large storage capacity, in the form of pumped storage systems or batteries, is needed to store surplus electricity from PV plants. At the same time, in the transport sector, the electric vehicle (EV) is being developed as an environmentally friendly vehicle. To promote the diffusion of EVs, we need infrastructure that can charge EVs in a short time; a battery-switch station is one solution to this problem. This study 1) proposes the use of the station batteries as a countermeasure for surplus electricity from PVs and 2) conducts two relevant analyses. In the first analysis, we calculate the marginal value of a battery and an inverter using the Optimal Generation Mix Model (OPTIGEN). In the second analysis, we set the annual lease fee for the inverter and the battery, and calculate the optimum installed capacity of these devices. The results showed that the marginal value of the inverter/battery decreases with increasing inverter/battery capacity. The optimum installed capacity of the inverter/battery is derived from the intersection of the line of marginal value with the line of the annual lease fee. The stations gain an additional profit by leasing batteries to utilities.

Economic Value of PV Energy Storage Using Batteries of Battery-Switch Stations

It is expected that energy management systems (EMS) on the demand side can be used as a method for enhancing the capability of balancing supply and demand of a power system under the anticipated increase of renewable energy generation such as photovoltaics (PV). Energy demand and solar radiation must be predicted in order to realize the optimal operation scheduling of demand side appliances by EMS, including heat pump water heaters, PV systems, and solar powered water heaters. This paper presents a day-ahead forecasting method for electricity consumption in a house to contribute to energy management. Ten forecasting methods are examined using real survey data from 35 households over a year in order to verify forecast accuracy. A daily battery operation model is also developed to evaluate the effect of load forecasts.

Short-term forecasting of residential building load for distributed energy management

High-resolution determinant analysis of Japanese residential electricity consumption using home energy management system data

High penetration of variable renewable generations such as Photovoltaic (PV) systems will cause the issue of supply-demand imbalance in a whole power system. Activation of residential power usage, storage and generation by sophisticated scheduling and control using the Home Energy Management System (HEMS) will be needed to balance power supply and demand in the near future. In order to evaluate the applicability of the HEMS as a distributed controller for local and system-wide supply and demand balances, we developed an optimum operation scheduling model of domestic electric appliances using mixed integer linear programming. Applying this model to one house with dynamic electricity prices reflecting the power balance of the total power system, it was found that the adequate changes in electricity prices bring about the shift of residential power usages to control the amount of the reverse power flow due to excess PV generation.

Yumiko Iwafune

Papers

Cooperative home energy management using batteries for a photovoltaic system considering the diversity of households

Economic Value of PV Energy Storage Using Batteries of Battery-Switch Stations

Short-term forecasting of residential building load for distributed energy management

High-resolution determinant analysis of Japanese residential electricity consumption using home energy management system data

Optimum operation scheduling model of domestic electric appliances for balancing power supply and demand