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This paper uses a one-dimensional, physics-based model of a valve-regulated lead-acid (VRLA) battery to examine the impact of demand response on uninterruptible power supply (UPS) availability in a datacenter. Datacenters are facilities that provide services such as cloud computing, web search, etc. They are also large electricity consumers. An energy-efficient 15 MW datacenter, for instance, may pay $1 m per month for electricity. Datacenters often utilize VRLA batteries to ensure high reliability in serving their computational demand. This motivates the paper's central question: to what extent does the use of datacenter UPS batteries for demand response affect their availability for their primary purpose (namely, emergency power)? We address this question using a physics-based model of the coupled diffusion-reaction dynamics of VRLA batteries. We discretize this model using finite differences, and simulate it for different datacenter battery pack sizes. The results show that for a typical datacenter power demand profile, a VRLA battery pack sized for UPS functionality can provide demand response with only a minimal loss of UPS availability. [ABSTRACT FROM AUTHOR]

This paper compares the work by Mark Jacobson et al. of 100% renewables to the rigors of long-run utility system planning. This comparison to integrated resource planning (IRP) allows comparison between assumptions used by Jacobson to results from real-world planning studies. Seven criteria are proposed for designing such a study. [ABSTRACT FROM AUTHOR]

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. [ABSTRACT FROM AUTHOR]

The increasing population living in cities is leading to higher resource utilization, which strains the cities’ ability to focus on sustainability. Adoption of different technologies can transform cities into “smart cities” that utilize energy in a more efficiently. This paper presents results from a technology assessment tool developed together with six mid-sized European cities. The main areas of focus have been evaluated based on the cities’ priorities: transportation (both public and private) and consumers’ perspectives on the use of smart electricity meters. The use of electric vehicles in Sweden, and a techno-economic evaluation of biogas-derived biomethane and electricity use in public transportation have been analyzed. The main conclusions show an overall higher efficiency for biogas-derived electricity use in electric buses; a need for higher consumer engagement through more detailed information provision for both increasing EV market penetration and electricity savings; and a need to establish detailed technology assessments for successful technology adoption in cities. [ABSTRACT FROM AUTHOR]

A rapid growth in interest in self-consumption of electricity generated by rooftop photovoltaic (PV) systems has been observed in recent years. This is fueled by decreasing levelized cost of electricity and feed-in tariffs for PV-systems as well as by increasing electricity prices, especially in the residential sector. Besides PV-battery systems, electrical heat pumps are a promising measure to increase self-consumption of electricity generated by distributed generation technologies in residential dwellings. However, little scientific research on the ability of heat pump systems to increase self-consumption of PV-electricity has been carried out so far. Therefore in this paper a mixed integer linear programing model for the optimal operation, system configuration and sizing for heat pump based house energy systems is developed and applied under different scenario conditions. The results show that the dimensioning of the heat pump is hardly influenced by the scenario assumptions, whereas the optimal sizing of PV is strongly dependent on the scenarios and in particular on the electrical load profiles. Over all scenario variations, the sizing of electrical and thermal storages is demand-driven and hardly any interdependencies with respect to optimal sizing can be observed between the different storage technologies. The availability of feed-in-tariffs generally yields large PV systems with high levels of self-sufficiency but low levels of self-consumption. Stricter feed-in-limitations lead to a reduction of the optimal PV systems size, but not to an increase in the optimal size of storage technologies. Over a wide range of base scenarios and scenario variations PV systems as well as additional flexibility options are part of the optimal system configuration, rendering it a promising and robust measure to decrease the operating cost of heat pump systems in the future. The results obtained in this study can provide valuable guidelines for manufacturers, installers and end-customers for the design of cost-effective self-consumption driven heat pump systems in the future. [ABSTRACT FROM AUTHOR]

Coal, a major source for electricity generation in many regions, is expected to keep its position among global energy sources in the next two decades. There are significant life cycle impacts of coal used for electricity generation such as heavy metal emissions and acidification. Impacts related to the combustion phase have so far been the focus of attention and are better documented than other phases of the life cycle. Relatively less attention has been paid to impacts of mining and coal preparation processes at mining sites due to generation of large quantities of wastewater, particulate matter and heavy metal emissions, and the use of heavy machinery which is energy-intensive. To this date, there are only a few studies specifically addressing impacts associated with the mining and coal preparation phases in the open literature. To further advance our understanding of the scale of impacts during these phases, this paper presents a life cycle assessment of lignite from extraction phase to the delivery to the power plant based on analysis of high quality data from twelve lignite mining sites that serve major lignite power plants in Turkey. For impacts on land use, four indicators which include erosion resistance, mechanical filtration, groundwater replenishment loss and biotic production, are estimated. Following life cycle impact assessment, two environmental performance improvement alternatives that can be easily implemented in mining sites are evaluated to quantify the potential degree of improvement. These alternatives include dewatering of the slurry waste and recovery of coal, and using biodiesel mixed fuel and watering roads to reduce dust formation. The analyses show that 40% improvement can be realized for ecotoxicity potential to water category through simple changes in mining practices. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) pricing serves as a cost-effective way to realize electricity demand response, which aims at relieving peak demand. Customer participation is critical to the success of TOU pricing programs. To fulfill the potential of such programs, customers must be able to access electricity tariffs and understand their terms. This paper reports a survey of 43 TOU pricing programs targeting industrial customers offered by U.S. utilities. This work is inspired by and complements the Federal Energy Regulatory Commission survey of demand response in the electric power industry, highlighting the interpretation of key pricing components and specific characteristics of TOU tariff sheets collected from public sources. The case studies examine various industrial scenarios to predict electricity cost savings when customers are facing the transition from flat rates to TOU pricing. The analysis results show that the cost savings vary enormously, ranging from −72.0% to +82.6%, depending on specific utility programs and switching strategies involved. Such information is useful for customers to determine whether to participate in a TOU pricing program. Key findings and implications for industrial customers, utilities, and regulatory agencies are also discussed. [ABSTRACT FROM AUTHOR]

Approximately 27% of the European energy consumption is caused by the domestic sector, where 19% of the end use energy demand is caused by electric devices. To investigate the factors at play, a stochastic bottom-up model for the generation of electric load profiles is introduced in this paper. The model is designed for investigating the effects of occupant behaviour, appliance stock and efficiency on the electric load profile of an individual household. For each activity of a person in the household, an electric appliance is used, and its electricity consumption is linked to measured electric load traces with a time resolution of 10 s. Probability distributions are incorporated for when and how often an appliance is operated. Duration of operation is given as probability density conditional on the start time. Shared use of an appliance by multiple persons is included in the model. Seasonal effects are considered by using changing probability sets during the course of the year. For validation, seven subgroups, which reflect typical household configurations, were formed and tested against measured field data from 430 households in 9 different cities across Germany. The results showed an accuracy of 91% and a correlation of up to 0.98. [ABSTRACT FROM AUTHOR]