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Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

According to the renewable energy directive 2009/28/EC, the European Union Member States should increase by 2020 the use of renewable energy to 20% of gross final energy consumption and to reach a mandatory share of 10% renewable energy in the transport sector. This study aims to quantify the impact of 2020 bioenergy targets on the land use in the EU, based on the projections of the National Renewable Action Plans in four scenarios: Scenario 1. Bioenergy targets according to NREAPs; Scenario 2. Bioenergy targets according to NREAPs, no second generation biofuels; Scenario 3. Bioenergy targets according to NREAPs, reduced import of biofuels and bioliquids; Scenario 4. Bioenergy targets according to NREAPs, high imports of biofuels and bioliquids. This study also considers the credit for co-products generated from biofuel production. The analysis reveals that the land used in the EU for bioenergy would range between 13.5 Mha and 25.2 Mha in 2020. This represent between 12.2% and 22.5% of the total arable land used and 7.3% and 13.5% of the Utilised Agricultural Area (UAA). In the NREAPS scenario, about 17.4 Mha would be used for bioenergy production, representing 15.7% of arable land and 9.4% of UAA. The increased demand from biofuels would lead to an increased generation of co-products, replacing conventional fodder for animal feed. Considering the co-products, the land used for bioenergy would range between 8.8 Mha and 15.0 Mha in 2020 in the various scenarios. This represent between 7.9% and 13.3% of the total arable land used in the EU and 4.7% and 8.0% of the UAA. In the NREAPS scenario, when co-products are considered, about 10.3 Mha would be used for biofuels, bioliquids and bioenergy production, representing 9.3% of arable land and 5.6% of agricultural land. This study further provides detailed data on the impact on land use in each Member State.

Abstract Building energy performance is often inadequate given design goals. While different types of assessment methods exist, they either do not consider design goals and/or are not general enough to integrate new and innovative energy concepts. Furthermore, existing assessment methods focus mostly on the building and system level while ignoring more detailed data. With the availability and affordability of more detailed measured data, the increased number of measured data points requires a structure to organize these data. This paper presents the Energy Performance Comparison Methodology (EPCM), which enables the identification of performance problems based on a comparison of measured data and simulated data representing design goals. The EPCM is based on an interlinked building object hierarchy that structures the detailed performance data from a spatial and mechanical perspective. This research is developed and tested on multiple case studies that provide real-life context and more generality compared to single case studies.

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Abstract The central role of occupants for achieving energy savings in residential buildings is increasingly recognised. Simulation programmes able to take into account occupant behaviour are considered to be powerful tools for bridging the gap between the predicted and the actual energy consumption for new buildings. Nevertheless, the majority of residential buildings that will constitute the housing stock in 2050 have already been built today, therefore occupant behaviour and building simulation tools need to be fully exploited for supporting the renovation of existing housing stock. The aim of this paper is to explore the role of occupant behaviour modelling in supporting decision-makers dealing with the design of renovation strategies for residential buildings. An Italian multi-family public housing building is assumed as case study to estimate the influence of three dimensions linked with occupant behaviour – management of the thermostat, management of the heating system, variation of building characteristics – on energy heating consumption. The results show that, while the occupant behaviour influences the heating loads up to 1/3 in case of high level of building retrofit, the less the building is renovated, the higher is the behavioural impact in absolute terms of energy reduction. Therefore, in order to be effective, renovation strategies are required to design appropriate informative instruments at an early stage to support behaviour changes towards responsible energy consumption.

Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Abstract This work, framed in the IEA SHC Task 51 “Solar Energy in Urban Planning”, presents an illustrative perspective of solar energy in urban planning through the analysis of 34 international case studies conducted in 10 countries. The aim here is to examine challenges, barriers and opportunities for active solar systems and passive solar strategies by taking into consideration interrelated technical and non-technical aspects in ongoing and completed projects. It focuses on exposing potential pitfalls and illustrating lessons learned in case studies divided into three categories: (i) existing urban areas, (ii) new urban areas, and (iii) solar landscapes. The analysis has yielded insights into the solar energy strategy adoption, the evaluation of solar energy production, solar irradiation and daylighting, and the architectural quality, sensitivity and visibility of the solar systems for urban planning. The outcomes have implications to stimulate successful practices in implementing solar strategies in urban planning and facilitating their replicability worldwide by avoiding common mistakes.

The Houston-Dallas (I-45) corridor is the busiest route among 18 traffic corridors in Texas, USA. The expected population growth and the surge in passenger mobility may result in a significant impact on the regional environment. This study uses a life cycle framework to predict and evaluate the net changes of environmental impact associated with the potential development of a high-speed rail (HSR) System along the I-45 corridor through its life cycle. The environmental impact is estimated in terms of CO2 and greenhouse gas (GHG) emissions per vehicle/passenger-kilometers traveled (V/PKT) using life cycle assessment. The analyses are performed referring to the Ecoinvent 3.4 inventory database through the phases: material extraction and processing, infrastructure construction, vehicle manufacturing, system operation, and end of life. The environmental benefit is evaluated by comparing the potential development of the HSR system with those of the existing transportation systems. The vehicle component, especially operation and maintenance of vehicles, is the primary contributor to the total global warming potential with about 93% of the life cycle GHG emissions. For the infrastructure component, 56.76% of GHG emissions result from the material extraction and processing phase (23.75 kgCO2eq/VKT). Various life cycle emissions of HSR except PM are significantly lower than for passenger cars.