Search

The building sector is currently responsible of 40% of global final energy consumption, influencing the broader energy system in terms of new electricity and heat capacity additions, as well as distribution infrastructure reinforcement. Current building energy efficiency potential is largely untapped, especially at the local level where retrofit interventions are typically enforced, neglecting their potential synergies with the entire energy system. To improve the understanding of these potential interactions, this paper proposes a methodology that links dynamic building simulation and energy planning tools at the urban scale. At first, a detailed bottom-up analysis was conducted to estimate the current and post-retrofit energy demand of the building stock. The stock analysis is further linked to a broader energy system simulation model to understand the impact of building renovation on the whole urban energy system in terms of cost, greenhouse gas emission, and primary energy consumption up to 2050. The methodology is suited to analyze the relationship between building energy demand reduction potential and clean energy sources&rsquo, deployment to shift buildings away from fossil fuels, the key priority for decarbonizing buildings. The methodology was applied to the case study city of Torino, Italy, highlighting the critical role of coupling proper building retrofit intervention with district-level heat generation strategies, such as modern district heating able to exploit low-grade heat. Being able to simulate both demand and supply future alternatives, the methodology provides a robust reference for municipalities and energy suppliers aiming at promoting efficient energy policies and targeted investments.

Electric appliances for cooling and lighting are responsible for most of the increase in electricity consumption in Karachi, Pakistan. This study aims to investigate the impact of passive energy efficiency measures (PEEMs) on the potential reduction of indoor temperature and cooling energy demand of an architectural campus building (ACB) in Karachi, Pakistan. PEEMs focus on the building envelope’s design and construction, which is a key factor of influence on a building’s cooling energy demand. The existing architectural campus building was modeled using the building information modeling (BIM) software Autodesk Revit. Data related to the electricity consumption for cooling, building masses, occupancy conditions, utility bills, energy use intensity, as well as space types, were collected and analyzed to develop a virtual ACB model. The utility bill data were used to calibrate the DesignBuilder and EnergyPlus base case models of the existing ACB. The cooling energy demand was compared with different alternative building envelope compositions applied as PEEMs in the renovation of the existing exemplary ACB. Finally, cooling energy demand reduction potentials and the related potential electricity demand savings were determined. The quantification of the cooling energy demand facilitates the definition of the building’s electricity consumption benchmarks for cooling with specific technologies.

In this paper, the proposed methodology minimizes the electricity cost of a laundry room by means of load shifting. The laundry room is equipped with washing machines, dryers, and irons. Additionally, the optimization model handles demand response signals, respecting user preferences while providing the required demand reduction. The sequence of devices operation is also modeled, ensuring correct operation cycles of different types of devices which are not allowed to overlap or have sequence rules. The implemented demand response program specifies a power consumption limit in each period and offers discounts for energy prices as incentives. In addition, users can define the required number of operations for each device in specific periods, and the preferences regarding the operation of consecutive days. In the case study, results have been obtained regarding six scenarios that have been defined to survey about effects of different energy tariffs, power limitations, and incentives, in a laundry room equipped with three washing machines, two dryers, and one iron. A sensitivity analysis of the power consumption limit is presented. The results show that the proposed methodology is able to accommodate the implemented scenario, respecting user preferences and demand response program, minimizing energy costs. The final electricity price has been calculated for all scenarios to discuss the more effective schedule in each scenario., This work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of ITEA 3 SPEAR Project 16001 and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017.

Concerns about negative consequences of gambling diffusion are increasing. Prevention and harm reduction strategies play a crucial role in reducing gambling supply and harms. This study aims to conduct an umbrella review of the effectiveness of gambling preventive and harm reduction strategies, which can be implemented at a local level and targeted at adults. It was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Sixteen reviews were analyzed, and 20 strategies were selected and classified in 4 areas with different targets and aims. Reducing the supply of gambling is an effective strategy both for the general population and for risky or problematic gamblers. Demand reduction interventions have been found to have limited effects but most of them are mainly focused on knowledge about risks and odds ratios. Risk reduction strategies aim to reduce contextual risk factors of the area where gambling is provided, change the gambling locations’ features, and modify individual behaviors while gambling. Smoking and alcohol bans or restrictions are considered one of the most effective strategies. Finally, harm reduction strategies targeted at problematic gamblers are potentially effective. Some relevant implementation conditions are identified and the results show inconsistent effects across different targets.

Urban street networks in the United States have been primarily designed for automobile traffic with negligible considerations to non-motorized transportation users. Due to environmental issues and quality of life concerns, communities are reclaiming street spaces for active modes and slowing the speeds in their downtown. Moreover, tactical urbanism, i.e., the use of street space for innovative purposes other than moving automobile traffic, is becoming attractive due to reduced automobile travel demand and the need for outdoor activities in the age of the COVID-19 pandemic. This study provides details of the modeling of an urban downtown network (in the City of San Jose) using microscopic traffic simulation. The model is then applied to evaluate the effectiveness of street design changes at varying demand scenarios. The microsimulation approach was chosen because it allows for the detailed modeling and visualization of the transportation networks, including movements of individual vehicles, bicyclists, and pedestrians. The street design change demonstrated here involves one-way to two-way street conversion, but the framework of network-wide impact evaluation may also be used for complete street conversions. The base conditions network was also tested under different travel demand reduction scenarios (10%, 20%, and 30%) to identify the corridors in the city network in which the tactical urbanism strategies (e.g., open-air dining) may be best accommodated. The study provides framework for the use of a microscopic model as part of a decision support system to evaluate and effectively implement complete streets/tactical urbanism strategies.

This paper reviews and analyses a decarbonization policy called the Tradable Energy Quotas (TEQs) system developed by David Fleming. The TEQs system involves rationing fossil fuel energy use for a nation on the basis of either a contracting carbon emission budget or scarce fuel availability, or both simultaneously, distributing budgets equitably amongst energy-users. Entitlements can be traded to incentivize demand reduction and to maximize efficient use of the limited entitlements. We situate this analysis in the context of Joseph Tainter’s theory about the development and collapse of complex societies. Tainter argues that societies become more socio-politically and technologically ‘complex’ as they solve the problems they face and that such complexification drives increased energy use. For a society to sustain itself, therefore, it must secure the energy needed to solve the range of societal problems that emerge. However, what if, as a result of deep decarbonization, there is less energy available in the future not more? We argue that TEQs offers a practical means of managing energy descent futures. The policy can facilitate controlled reduction of socio-political complexity via processes of ‘voluntary simplification’ (the result being ‘degrowth’ or controlled contraction at the scale of the physical economy).

This article belongs to the Special Issue Demand Response in Smart Grids, System operators have moved towards the integration of renewable resources. However, these resources make network management unstable as they have variations in produced energy. Thus, some strategic plans, like demand response programs, are required to overcome these concerns. This paper develops an aggregator model with a precise vision of the demand response timeline. The model at first discusses the role of an aggregator, and thereafter is presented an innovative approach to how the aggregator deals with short and real-time demand response programs. A case study is developed for the model using real-time simulator and laboratory resources to survey the performance of the model under practical challenges. The real-time simulation uses an OP5600 machine that controls six laboratory resistive loads. Furthermore, the actual consumption profiles are adapted from the loads with a small-time step to precisely survey the behavior of each load. Also, remuneration costs of the event during the case study have been calculated and compared using both actual and simulated demand reduction profiles in the periods prior to event, such as the ramp period, The present work was done and funded in the scope of the following projects: UIDB/00760/2020, CEECIND/02887/2017, and COLORS PTDC/EEI-EEE/28967/2017, funded by FEDER Funds through the COMPETE program. This work has also received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066).

Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers.

Daylight usage in buildings improves visual comfort and lowers the final energy demand for artificial lighting. The question that always occurs is how much conservation can be achieved? New or rare materials and constructions have a lack of information about their application. Therefore, the current investigation quantifies the daylight and energy performance of a rare multi-layer textile membrane roof. A translucent, thermal insulation with a glass fibre fleece between the two roof membranes combines daylight usage and heating demand reduction. A sports hall built in 2017 is used as a case study building with 2300 m2 membrane roof surface. The optical properties of the roof construction were measured with a total visual light transmittance τv of 0.72% for a clean surface. A climate-based annual daylight modelling delivers daylight indicators for different construction scenarios. The results show that, in comparison to only one glass façade, the additional translucent and thermally insulated membrane roof construction increases the annual daylight autonomy (DA700) from 0% to 1.5% and the continuous DA700 from 15% to 38%. In the roof-covered areas of the sport field, this results in a 30% reduction of the electricity demand for artificial lighting from 19.7 kWhel/m2/a to 13.8 kWhel/m2/a, when a dimming control is used. The study also found that the influence of the soiling of one layer decreases its light transmittance by a factor 0.81. Two soiled layers lower τv by a factor of 0.66 to 0.47%. This increases the electricity demand for lighting by only 12%. The results should be very valuable as a comparison and benchmark for planners and future buildings of a similar type.

The advent of advanced metering infrastructure (AMI) generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL) estimation in demand response (DR) management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM) and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities’ residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate.

Under the present electricity tariff structure in Malaysia, electricity billing on a monthly basis for commercial and industrial consumers includes the net consumption charges together with maximum demand (MD) charges. The use of batteries in combination with photovoltaic (PV) systems is projected to become a viable solution for energy management, in terms of peak load shaving. Based on the latest studies, maximum demand (MD) reduction can be accomplished via a solar PV-battery system based on a few measures such as load pattern, techno-economic traits, and electricity scheme. Based on these measures, the Maximum Demand Reduction (MDRed) Model is developed as an optimization tool for the solar PV-battery system. This paper shows that energy savings on net consumption and maximum demand can be maximized via optimal sizing of the solar PV-battery system using the MATLAB genetic algorithm (GA) tool. GA optimization results revealed that the optimal sizing of solar PV-battery system gives monthly energy savings of up to 20% of net consumption via solar PV self-consumption, 3% of maximum demand (MD) via MD shaving and 2% of surplus power supplied to grid via net energy metering (NEM) in regards to Malaysian electricity tariff scheme and cost of the overall system.

The ambitious targets for renewable energies in Germany indicate that the steady growth of installed capacity of the past years will continue for the coming decades. This development is connected with significant material flows—primary material demand as well as secondary material flows. These flows have been analyzed for Germany up to the year 2050 using a statistical model for the turbines’ discard patterns. The analysis encompasses the flows of bulk metals, plastics, and rare earths (required for permanent magnets in gearless converters). Different expansion scenarios for wind energy are considered as well as different turbine technologies, future development of hub height and rotor diameter, and an enhanced deployment of converters located offshore. In addition to the direct material use, the total material requirement has been calculated using the material input per service unit (MIPS) concept. The analysis shows that the demand for iron, steel, and aluminum will not exceed around 6% of the current domestic consumption. The situation for rare earths appears to be different with a maximum annual neodymium demand for wind energy converters corresponding to about a quarter of the overall 2010 consumption. It has been shown that by efficiently utilizing secondary material flows a net material demand reduction of up to two thirds by 2050 seems possible, (i.e., if secondary material flows are fully used to substitute primary material demand).

In the UK, water supplies are under pressure from climate, population and lifestyle change. Showering is the largest component of domestic water consumption. Young adults are high water-users at a transitional life-stage, when practices are dynamic, and habits shaped. This paper presents the methodology, early findings and reflections on challenges of working with different data types and scales, to explore real-world water-saving through a mixed-methods approach, focusing on showering patterns of first year university students in campus accommodation at the University of the West of England, Bristol, UK. Combining household meter, logged water-fixture micro-component, personal-use questionnaire, user diary and stakeholder focus group data with the Scottish Government Individual-Social-Material model, typical showering demand reduction interventions were evaluated and insights into alternative interventions were generated. Results indicate Estates&rsquo, routine equipment maintenance and database management affect data quality and consistency. Despite these issues a profile of daily student water use was derived (equivalent to 114 litres per person per day) but with high variability between different households (from 83 to 151 litres per person per day). Average shower durations (self-reported 10&ndash, 12 minutes) were higher than reported UK norms, although frequency was similar to the UK daily shower norm. Average measured shower volumes (51 litres in one house) were not excessive, indicating shower fixtures provided a contribution to water saving.

The role of Demand Side Management (DSM) with Distributed Energy Storage (DES) has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS) algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU) pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR) demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness.