The power supply resiliency of residential feeders against grid outages can be enhanced by installing battery energy storage (BES). Most of the previous studies used distributed BES (DBES) to ...increase resiliency of the feeders. The cloud BES (CBES) can also be used in a similar manner to improve resiliency. However, detailed modeling of optimal CBES sizing for resiliency improvement has not sufficiently been investigated in the literature. This article formulates an optimization problem to determine the optimal CBES size to improve resiliency against grid outages. Both the number of outages and the outage durations are carefully incorporated into the problem. The results obtained by the proposed method with the optimal CBES size are also compared with those found for the optimal DBES size. It has been found that, for the studied feeder, the net present cost (NPC) of CBES decreased by 25.49% compared to its counterpart DBES. When the number of grid outages and/or the outage durations are increased, the CBES is found to be more economical than the DBES for all cases. For a budget limit of 750,000, the maximum interrupted demand is found as 39 kWh and 1,351 kWh for the CBES and DBES, respectively. When the budget limit is increased to 1 M, the interrupted demand for the CBES is reduced to zero whereas the DBES still needs to interrupt a maximum demand of 805 kWh.
The causes of fire in commercial and residential communities are complex, rescue is difficult, once a fire occurs, the loss is difficult to estimate, and a reasonable fire risk assessment needs to be ...made. This paper establishes a complete index system for fire risk assessment in commercial and residential communities, establishes a cloud model by using the ISM-Shapley value method, evaluates the safety level of a commercial and residential community in Jinan, and puts forward corresponding countermeasures, which provides a quantifiable analysis method for fire risk analysis.
Residential communities are the basic living units in Chinese cities. Housing prices are closely associated with the community location and surrounding support facilities. When selecting satisfactory ...residential accommodation, potential real estate purchasers prioritize the community location in a city at the macro-level and then consider other micro-factors (i.e., the floor, orientation, structure, etc.). This paper attempts to explore the relationship between housing prices and locational factors at the community level. We collect the current market prices of 545 residential communities built in the last decade in Ningbo, the second largest city in Zhejiang Province. Then, thirteen locational factors of five dimensions are identified to research their influences on housing prices. In the process of selecting certain locational variables, both extant features and additional features (i.e., planned ones) are considered. The geographic field model is introduced to quantify the external effects of locational factors, due to its advantages of producing more accurate results than that of traditional distance-based measure methods. Then, regression analysis is performed based on the average housing prices of residential communities and explanatory variables by the ordinary least squares model and the geographically weighted regression. The regression coefficients demonstrate that the externalities of parks, lakes, department stores, banks, secondary schools and rail transit have significant but spatially non-stationary effects on housing prices. The results provide references for local real estate planning departments and potential real estate purchasers.
•This paper explores the relationship between the locational factors' externality and housing prices at the community level.•The geographic field model is introduced to quantify explanatory variables for more accurate results.•The magnitude and spatial heterogeneity are observed in the influence of different locational factors on housing prices.•Even rail transit in the planning stage can still have a significant effect on housing prices.•The natural landscape features must be of a certain size or quality to exert an economic influence on housing prices.
Short-term household electricity load forecasting is important for utility companies to ensure reliable power supplies. Traditional methods for load forecasting relied on historical records from one ...single data source and have limitations with insufficient or missing data. Recently, an emerging family of machine learning algorithms, multitask learning (MTL), has been developed and has the potential for load forecasting. By MTL, the electricity consumption data from multiple communities can be fused to improve forecasting accuracy. However, appropriate modeling of the relatedness to enable the between-community knowledge transfer remains a challenge. This paper proposes an improved MTL algorithm for a Bayesian spatiotemporal Gaussian process model (BSGP) to characterize the relatedness among the different residential communities. It hypothesizes on the similar impacts of environmental and traffic conditions on electricity consumption in improving short-term load forecasting. Furthermore, this paper proposes a low-ranked dirty model along with an iterative algorithm to improve the learning of model parameters under an MTL framework. This paper uses a real-world case study from two residential communities in Tallahassee, Fl, USA, to demonstrate the method effectiveness. The proposed method significantly outperforms state-of-the-art forecasting methods and effectively captures the relatedness to provide between-community knowledge transfer compared with other MTL methods.
Nanogrid (NG) cluster (NGC) has the potential to act as one type of basic structure for the future low voltage distribution networks. In this paper, an online energy sharing method is proposed for ...improving the self-sufficiency and photovoltaic (PV) consumption of NGC. First, a hybrid cyber-physical peer-to-peer (P2P) energy sharing framework is proposed, which is a combination of P2P physical system (i.e., NG-to-NG) and client-server cyber system (i.e., NG controllers-central controller). Moreover, an energy sharing strategy with the classification of energy exporting and importing NGs is designed. Considering the stochastic features of PV energy and end user load, an online optimization model and algorithm is formulated based on Lyapunov optimization, as to maximize the self-sufficiency and guarantee the stability of energy storage queues. Finally, in a case study using the realistic data from the residential community, numerical experiments show the effectiveness of the proposed method in improving the self-sufficiency of NGC.
Significance
Researchers have been successful in inducing environmental conservation behavior, but the effect is usually transient. Persistence in behavior change has been elusive. We developed a ...simple, cost-effective, and replicable behavioral intervention for household water conservation that achieved persistence of behavior change. Our method uses objectively defined, sustainability-based per-person goals rather than comparative social norms that can themselves be beyond the threshold of biophysical sustainability or policy goals. Per-person goals help transcend the limitations of extant social comparison interventions that ignore household size. This work also expands the scope of behavioral interventions to settings where resource pricing is not possible or is difficult.
Achieving persistence in household behavior modification has been a central but elusive goal of environmental conservation attempts that rely on behavioral interventions. We implemented a habit change intervention, designed to achieve persistent change in household water conservation behavior in an affluent residential community in urban India. We found a 15 to 25% reduction in household water consumption in the absence of any volumetric pricing. Most importantly, the effects of our 5-wk intervention persisted for more than a year, after which marginal pricing was introduced. The treatment gap was not bridged even after a year under the marginal price regime.
Ongoing reductions in the cost of solar photovoltaic (PV) systems are driving increased installations by residential households. Various incentive programs such as feed-in tariff, net metering, net ...purchase, and sale that allow the prosumers to sell their generated electricity to the grid are also powering this trend. In this paper, we investigate sharing of PV systems among a community of households, who can also benefit further by pooling their production. Using cooperative game theory, we find conditions under which such sharing decreases their net total cost. We also develop allocation rules such that the joint net electricity consumption cost is allocated to the participants. These cost allocations are based on the cost causation principle. The allocations also satisfy the standalone cost principle and promote PV solar aggregation. We also perform a comparative analytical study on the benefit of sharing under the mechanisms favorable for sharing, namely net metering, and net purchase and sale. The results are illustrated in a case study using real consumption data from a residential community in Austin, TX, USA.
Major pathways of antibiotics release into the environment which cause antibiotic resistance for human.
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•Hospital wastewater were used for real sample analysis in the current ...work.•Removal of CIP was successfully optimized using response surface methodology.•Electrical energy consumption at optimum operating conditions was 0.613kWhm−3.
Pharmaceuticals as severe contaminants of surface and ground water around the manufacturing communities and residential zones received growing attention recently. Since, there is no report on ciprofloxacin (CIP) removal using electrocoagulation (EC) process by aluminum electrodes, the present work deals with efficient removal of CIP from hospital wastewater using mentioned method. Response surface methodology (RSM) was used to evaluate the main effects of parameters, their simultaneous interactions and quadratic effect to achieve the optimum condition for EC process. According to the obtained results from regression analysis, it was found that the experimental data are best fitted to the second-order polynomial model with coefficient of determination (R2) value of 0.9086, adjust correlation coefficient (Adj. R2) value of 0.8796 and predicted correlation coefficient (pred. R2) value of 0.7834. EC process was applied successfully with removal efficiency of 88.57% under optimal operating condition of pH 7.78, inter-electrode distance 1cm, reaction time 20min, current density 12.5mAcm−2 and electrolyte dose of 0.07M NaCl with the initial CIP concentration of 32.5mgL−1. The experimental efficiency was in satisfactory agreement with the predicted efficiency of 90.34%. The obtained results revealed that, sweep flocculation as a determinant mechanism controlled the adsorption of CIP molecules on aluminum hydroxide precipitates. Electrode consumption and electrical energy consumption were found to be 66.80gm−3 and 0.613kWhm−3, respectively. The obtained results from real sample analysis revealed that the initial CIP concentration of 154±6μgL−1 of hospital wastewater were found to reached to zero after applying optimal condition of EC process.
A prior knowledge of residential load demand is critical for power system operations at the distribution level, such as economic dispatch, demand response and energy storage schedule. However, as ...residential customers perform more casual and active consumption behaviors, prediction of such highly volatile loads can be much harder. Owing to the development of sensor technology, micrometeorological data can be sampled with a high geographic resolution. Those data that represent the weather condition on the land surface show a strong relationship to the residential load evidently, whereas it remains unsolved on how to fully utilize those great number of datasets. This paper proposes a day-ahead probabilistic residential load forecasting method based on a novel deep learning model, named convolutional neural network with squeeze-and-excitation modules (CNN-SE), and micrometeorological data. The model can employ multi-channel input data with dissimilar weights, suitable for analyzing massive relevant input factors. A feature extraction method is adopted for customer consumption pattern based on sparse auto-encoder (SAE), which can help correct probabilistic forecasting results. A case study that covers 8 residential communities and 18 micrometeorological sites is conducted to validate the feasibility and accuracy of the proposed hybrid method.
In this paper, a systematic analysis approach is outlined to design carbon neutral and resilient residential communities in Tabuk region, Saudi Arabia. The design used cost optimization approach to ...determine the required capacities for on-site power generation and energy storage using renewable energy resources to achieve desired carbon neutrality and energy resiliency thresholds. Moreover, different energy efficiency levels for the housing units are considered to design grid-connected residential communities based on prevalent grid electricity prices and carbon emissions. The analysis indicates that combinations of energy efficiency measures, PV systems, and storage batteries can be utilized to achieve both desired carbon neutrality and energy resiliency levels. The implementation of proven energy efficiency measures can reduce by over 45 % both the annual electricity demands, and the on-site PV capacities needed to reach carbon neutral designs for the residential communities. The cost-effectiveness of carbon-neutral and resilient designs depends significantly on installation costs of PV systems and wind turbines as well as electricity prices purchased and sold back to the grid. The levelized cost of energy for net-zero energy communities can be as low as 0.06 USD/kWh to reach carbon neutrality.
•A cost optimal design approach for carbon neutral and resilient communities is developed.•The approach is applied to design and operate residential communities in the Tabuk region.•The application of energy efficiency is found to be a crucial step to reach carbon neutrality.•The cost-effectiveness of carbon neutrality and resiliency depend highly on grid electricity prices.
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