Search

Therapeutic communities face high drop-out rates and general distrust of their effectiveness among substance users. Actively involving users early in treatment promotes greater compliance with the treatment and is predictive of better outcomes. However, users often occupy a passive and subordinate role, exacerbated by the lack of research that explores their perspectives, beliefs, and experiences. This study examined the discourses of 57 consumers who were part of a community for less than 15 days, investigating the meanings attributed to service entry and treatment. A protocol of four written open-ended questions was employed and analysed through discourse analysis and positioning theory. The results indicate that participants configure the community as a place symbolically and spatially distinct from the rest of the world, where they isolate themselves to seek support during times of extreme difficulty. However, what they are seeking is a solution to acute distress caused by substance use, intertwined with social, economic, and relational issues. The concept of treatment is built on the image of the substance user as an individual making a weak request for help, attributing the problem solely to drugs and exhibiting reduced agency in addressing their issues. The collected texts provide a better understanding of the experiences of new users, highlighting the importance of co-constructing personalised projects that empower consumers to feel actively involved in their own change, exploring their theories and definitions of self to structure pathways based strictly on their needs. [ABSTRACT FROM AUTHOR]

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of disasters further contribute to the complexity. Despite these challenges, the importance of timely evacuations lies in safeguarding human safety, enabling rapid disaster response, preserving critical infrastructure, and reducing economic losses. Overcoming these hurdles necessitates comprehensive planning, investment in resilient infrastructure, effective communication strategies, and continuous community engagement to foster preparedness and enhance evacuation efficiency. This research looks into the complexities of evacuation dynamics within urban residential areas, placing a particular focus on the interaction between joint-rental arrangements and family ties and their influence on evacuation strategies during emergency situations. Using agent-based modeling, evacuation simulation scenarios are implemented using the Changhongfang community (Shanghai) while systematically exploring how diverse interpersonal relationships impact the efficiency of evacuation processes. The adopted methodology encompasses a series of group experiments designed to determine the optimal proportions of joint-rental occupants within the community. Furthermore, the research examines the impact of various exit selection strategies on evacuation efficiency. Simulation outcomes shed light on the fundamental role of interpersonal factors in shaping the outcomes of emergency evacuations. Additionally, this study emphasizes the critical importance of strategic exit selections, revealing their potential to significantly enhance overall evacuation efficiency in urban settings. [ABSTRACT FROM AUTHOR]

"Sharing economy" refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach. [ABSTRACT FROM AUTHOR]

Shattuck St. Mary's School (SSM) is a cohabitating residential community of multiple dormitories with approximately 350 full-time boarding students in Faribault, Minnesota. This article examines factors that may have impacted SSM's COVID-19 positive testing data. Further, it compares the school's figures with the surrounding areas of Rice County, Minnesota, and the United States' COVID-19 testing trends from August 2020 to April 2022. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Therapeutic communities face high drop-out rates and general distrust of their effectiveness among substance users. Actively involving users early in treatment promotes greater compliance with the treatment and is predictive of better outcomes. However, users often occupy a passive and subordinate role, exacerbated by the lack of research that explores their perspectives, beliefs, and experiences. This study examined the discourses of 57 consumers who were part of a community for less than 15 days, investigating the meanings attributed to service entry and treatment. A protocol of four written open-ended questions was employed and analysed through discourse analysis and positioning theory. The results indicate that participants configure the community as a place symbolically and spatially distinct from the rest of the world, where they isolate themselves to seek support during times of extreme difficulty. However, what they are seeking is a solution to acute distress caused by substance use, intertwined with social, economic, and relational issues. The concept of treatment is built on the image of the substance user as an individual making a weak request for help, attributing the problem solely to drugs and exhibiting reduced agency in addressing their issues. The collected texts provide a better understanding of the experiences of new users, highlighting the importance of co-constructing personalised projects that empower consumers to feel actively involved in their own change, exploring their theories and definitions of self to structure pathways based strictly on their needs.

Renewable energy resources, especially rooftop solar PV, have gained momentum during the past few years. However, the local consumption of PV power is limited due to the negative correlation between peak PV power and residential loads. Therefore, this study analyzes various cases to maximize the consumption of renewables in communities encompassing dwellings both with and without PV installations. The three cases considered in this study are local energy storage, community energy storage, and internal trading. A total of six cases are analyzed by evaluating these cases individually and in combinations. To achieve this, first, a generalized optimization model with specific constraints for each case is developed. Subsequently, different indices are devised to quantitatively measure trading with the grid and the consumption of renewables under varying cases. The performance of these different cases is analyzed for a community comprising five dwellings over a summer week. Furthermore, the performance of each case is evaluated for various seasons throughout the year. Additionally, a sensitivity analysis of different storage capacities (both local and community) is conducted. Simulation results indicate that community storage results in the highest renewable consumption if only one case is considered. However, the overall combination of internal trading and community storage results in the highest cost reduction, lowest dependence on the grid, and the highest consumption of renewables. Finally, a techno-economic analysis is performed on four widely used battery technologies, taking into account diverse cost and technical considerations. [ABSTRACT FROM AUTHOR]

“Sharing economy” refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach.

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of disasters further contribute to the complexity. Despite these challenges, the importance of timely evacuations lies in safeguarding human safety, enabling rapid disaster response, preserving critical infrastructure, and reducing economic losses. Overcoming these hurdles necessitates comprehensive planning, investment in resilient infrastructure, effective communication strategies, and continuous community engagement to foster preparedness and enhance evacuation efficiency. This research looks into the complexities of evacuation dynamics within urban residential areas, placing a particular focus on the interaction between joint-rental arrangements and family ties and their influence on evacuation strategies during emergency situations. Using agent-based modeling, evacuation simulation scenarios are implemented using the Changhongfang community (Shanghai) while systematically exploring how diverse interpersonal relationships impact the efficiency of evacuation processes. The adopted methodology encompasses a series of group experiments designed to determine the optimal proportions of joint-rental occupants within the community. Furthermore, the research examines the impact of various exit selection strategies on evacuation efficiency. Simulation outcomes shed light on the fundamental role of interpersonal factors in shaping the outcomes of emergency evacuations. Additionally, this study emphasizes the critical importance of strategic exit selections, revealing their potential to significantly enhance overall evacuation efficiency in urban settings.

The harsh climate and the aging of urban populations have negatively impacted the quality of life of the elderly in severely cold regions. As a result, there is an urgent need to improve environment quality and accurately evaluate outdoor thermal comfort. However, existing studies have paid little attention to older adults living in severely cold climates. This paper aims to fill this gap by studying the elderly population in severely cold cities with high aging rates in China. By combining on-site testing, questionnaire surveys, CFD modeling, parametric computing, and statistical analysis, the study presents an adapted UTCI for elderly people, as well as multi-seasonal prediction models. The results (1) show that the neutral ranges of the UTCI are significantly affected by both climate zones and age groups. Older people are more tolerant to heat but more sensitive to cold. (2) The results also reveal the importance of factors such as air temperature, wind speed, solar radiation temperature, wind direction, relative humidity, and cloud cover in evaluating outdoor thermal comfort. (3) Multi-seasonal thermal comfort models based on neural networks were developed, and empirical studies verified that the model had the highest accuracy in the transitional season and the lowest accuracy in the winter season. [ABSTRACT FROM AUTHOR]

Analyzing and understanding the electricity consumption of end users, especially the anomalies (outliers), are vital for the planning, operation, and management of the power grid. It will help separate the group of users with unpredictable consumption behavior and then develop and train specialized deep learning models for power load forecasting or regular and non-regular users. The aim of the current work is to divide electricity customers into numerous groups based on anomalies in consumption behavior and major clusters. Successful separation of such groups of customers will provide us with two advantages. One is the increase in the accuracy of load forecasting of other users or groups of users due to their predictable consumption behavior. The second is the opportunity to develop and train specialized deep learning models for customers with highly unpredictable behaviors. The novelty of the work is the segregation of anomalous electricity users from normal/regular users based on outliers in their past power consumption behavior over a period of 92 days. Results indicate that almost 85 percent of the users in the selected residential community attribute one major cluster in their consumption behavior over a period of 3 months of data (92 days). It is also evident from the results that only a small proportion of customers, i.e., 10 out of 69 customers (15 percent), have either more than one cluster or attribute no cluster (zero clusters), which is highly important and indicates that these are the possible users who cause higher variations in power consumption of the residential community.

• Energy consumption based Battery Energy Storage and rooftop Solar PV sizing. • Typical high-end units consumes 22% more than the medium-cost units and 56% more than low-cost units. • Community BESS and rooftop Solar PV has to be sized at maximum or 125% of maximum to supply for VPP. • More n R is needed if sizing is based on max E C while lesser n R if size is based on 125% of the max E C. Virtual Power Plants (VPP) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid. They will thus facilitate the greater democratization of the energy systems. VPP is an aggregation of renewable energy technologies, such as solar PV. It is usually operated with battery energy storage system (BESS) facilities to solve the variability issue of solar PV. However, BESS used as part of VPP has to be appropriately sized to work in the electrical network. While VPP operated with BESS may provide significant technical and financial advantage, most of the BESS sized to be used as VPP in the literature are large scale, operated with large scale VRE's in the grid. Some of the literatures also provide the use of BESS sizing with Solar PV but for shared residential community use only. The context of BESS sizing for VPP from a residential community is not prevalent in the literature. In this paper, the context of a sizing BESS for VPP from a residential neighbourhood is studied. It is aimed that by optimally sizing BESS for a residential community with rooftop solar PV can supply power to the community and any excess energy may be used for VPP application for the grid. This paper provides a practical process for evaluating the proper size of a centralized BESS in a community with rooftop Solar PV by considering the energy consumption and peak demand profiles of the residential units in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and for any excess energy to support the electrical networks reliability improvement. The result shows that using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for VPP application, particularly electrical networks reliability improvement by supplying for the Energy not Supplied (EnS), E N. The result provides that for a High-end community, the stored energy in the BESS can reach up to 24.578 MW h using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17.549 MW h and 14.527 MW h using 125% of maximum sizing at either mean or maximum energy consumption, respectively. Moreover, the result also shows that to alleviate Energy not Supplied (EnS) and contribute to reliability improvement, more residential units are needed if sizing is based on maximum energy consumption, while a lesser number of residential units if the size is based on 125% of the maximum energy consumption. [ABSTRACT FROM AUTHOR]

Driven by a large digital simulation environment, CFD calculation software is used to simulate test results so that they can be effectively applied to engineering practice. This paper explores the suitable outdoor wind environment for residential areas in the hot summer and the cold winter. Taking Xishan Huijing in Changsha as an example, the most unfavorable wind field environment is used as the boundary condition, and the optimal design mode for this residential area is explored based on the Butterfly platform. The research in this paper is mainly divided into five steps: (1) using Rhino 7.0 to establish a 3D model; (2) using the Butterfly 0.0.05 platform to simulate the wind field and export the data at the same time, and to realize the preview through the visualization method; (3) processing the exported simulation data and the calculation of related indices; (4) applying the entropy-based TOPSIS method on the MATLAB 2020 platform to rank the preferred scheme and obtain the corresponding index weights; and (5) using a K-means cluster analysis on SPSS 26 software to optimize the scheme. The results show that (1) the wind environment quality will be overestimated in the summer if the influence of neighboring buildings on the site is not considered, while the opposite is true in the winter, with the error of both reaching two times. (2) The weights of the indicators determined by the TOPSIS entropy weighting method indicate that wind protection in the winter should be prioritized over natural ventilation in the summer in this area. The maximum wind speed in the winter has the most significant weight, reaching 0.287, and the uniformity of the wind field in the summer is the most important, reaching 0.1102. (3) In the layout design of the residential district, the staggered layout of the 60 m high slab houses in the northern, northeastern, and northwestern directions of the site creates a better wind field environment, which attains the highest score by the TOPSIS entropy weighting method, reaching 0.1539, with the second highest score, reaching 0.1511, for the layout method. The research results will provide a scientific basis for the design of residential buildings in the hot-summer and cold-winter regions in China, and also help designers to better grasp the outdoor wind environment of residential buildings in the pre-design stage. [ABSTRACT FROM AUTHOR]

To relieve the impact of the power grid outages on the residential sector, residential building energy management under grid outage events has been becoming the research hotspot in both the academic and industry. Considering the coordination of electric vehicles (EVs) and household load flexibility, this paper proposes a two-stage energy management approach for residential community under the planned outages. In the optimal scheduling stage, after receiving the information of the planned outage from the grid, the EVs’ charging–discharging power and community load curve reshaping schemes are optimal determined by residential community energy management system (CEMS), aiming at to minimize the total amount of unserved load of the whole community over the planned outage horizon. In the power allocation stage, with the formulated power allocation model, the determined residential community load curve from the above stage is then allocated to each house. The numerical test is finally conducted and the results validated the effectiveness and feasibility of the proposed approach.

Community environments are important factors affecting seniors' daily mobility; however, how environments work still lacks a deep understanding. Therefore, this study selected two of these residential communities in China and explored the relationship between the daily mobility of the elderly and their communities. Through semi-structured interviews, GPS data, and individual case-based analysis, the daily movements and outdoor activities of the older adults in these communities were analyzed. The results show that different communities offer inequable environments for seniors, resulting in their inconsistent daily mobility. As the outcome of person-environment fit, daily mobility is influenced by physical, social, and service aspects of an age-friendly environment. The influences reflect on the objective movement and subjective experience of daily mobility. Specifically, the spatiotemporal distribution of different outdoor activities and their corresponding subjective well-being depend on the fit of the community environment. This study contributes to a better understanding of the daily mobilities of older adults in their living environment. [ABSTRACT FROM AUTHOR]

Background: Residential green spaces and the co-inhabiting bird communities provide critical cultural ecosystem services for urban dwellers, which is critical for citizens' psychological well-being but has largely been overlooked, especially in China. This study attempted to delineate the relationship between human-nature interactions occurring at different scenarios in the health deriving procedure, and identify the key factors influencing urban residents' psychological well-being and the potential critical human-nature connections within their living spaces. Methods: Bird, plant, and social surveys were sequentially conducted. A model selection procedure based on AICc information was used to find the key factors that are responsible for residents' psychological well-being, and logistic regression analysis was conducted to figure out the key drivers for the occurrence of critical within-living-space human-nature connections. Results: Most of the respondents are aware of the neighborhood biodiversity and most of them are fond of it, and respondents who prefer fauna over flora have a considerably lower depression degree. Watching birds is the only critical within-living-space nature-related activity that is responsible for residents' psychological well-being. Both residents' psychological well-being and bird watching activity were simultaneously affected by nature-related activities within and outside the living spaces, while their preference for different aspects of biodiversity and their capability in perceiving them were also important, despite the difference in exact parameters. In addition, personal attributes such as age, financial income, and physical or mental health status also contribute to psychological well-being. Conclusions: This study successfully bridged citizens' minds with health products, and the major contribution was the demonstration of a spatial-scale-oriented circuit for people-nature connections, which contributes to the practical application of nature-based therapy. [ABSTRACT FROM AUTHOR]

Abstract It is recognized by academia and industry that second‐life batteries retired from electric vehicles still have use values and can be effectively used for supporting less demanding applications. At present, there lacks investigation on the applications of re‐using retired batteries on serving residential sector's energy management. Motivated by this, this paper studies the scenario of assembling retired batteries to be second‐life battery energy storage systems (SL‐BESSs) and using them to serve the energy demand of residential communities in an affordable manner. Based on an established SL‐BESS model, a two‐level community energy management framework is proposed, which optimizes the schedules of a SL‐BESS and other energy resources in a community subjected to a variety of short‐term operational objectives. In the upper level, a many‐objective optimization model is formulated, which comprehensively integrates four objectives covering the community's multi‐scale operational considerations. A NSGA‐III‐based solving approach is developed to find the non‐dominated solutions of the model. In the lower level, the optimal community scale load reshaping decisions and energy costs obtained from the upper level are allocated to individual houses. Extensive numerical case studies are conducted, and the results show that the proposed system can realize better trade‐off among the different operational considerations with less computational cost.

Transactive energy (TE) provides joint market and control functionality to manage distributed energy resources (DERs) in distribution networks. This work develops a real-time TE management framework that allows residential customers to actively join in the real-time transactive market with fulfilling households’ preferences including comfort, economical energy consumption, and privacy-preserving. In this regard, first, a user-friendly algorithm is developed to calculate the real-time willingness to pay (bid) for electric vehicles (EVs) and heating, ventilation, and air conditioning (HVAC) units considering customers’ preferences and concerns. Then, to preserve the privacy of households, the centralized market-clearing problem to maximize social welfare is decomposed into several subproblems using the alternating direction method of multipliers (ADMM) approach. Also, closed-form solutions to all subproblems are derived to simplify implementation and mitigate the computational complexity instead of solving optimization subproblems directly. This model is then implemented in a case study with several numbers of smart homes. The numerical results illustrate that the proposed distributed transactive model not only satisfies households’ comfort preferences but also decreases the average charging cost of EV batteries by 40% compared to the uncontrolled charging model. Further, the results show that our proposed model significantly mitigates the computational burden of the transactive market clearing problem compared to the centralized approach and the distributed approach without closed-form solutions.

The increasing penetration of distributed renewable energy and electric vehicles (EV) in local microgrids/residential-community has brought a great challenge to balancing system stability and economic benefits. This paper proposes a decentralized framework based on an efficient federated deep reinforcement learning method for plug-in electric vehicle (PEV) fleet charging management in a residential community, which is equipped with a photovoltaic and battery energy storage system and connected to a local transformer. Firstly, the framework of PEV charging management is described as a virtual EV charging station coordinating charging tasks through sharing public information with distributed agents. Then, an individual preference model of PEV is developed considering heterogenous PEV charging anxiety, battery degradation, and collective penalty. Subsequently, we propose an attention-weighted federated soft-actor-critic method to efficiently seek the co-ordinational scheduling of the PEV fleet charging in a distributed way, where scalability and privacy protection can be ensured with attention-based information sharing. Finally, a real-world case study is conducted to validate the effectiveness and feasibility of the proposed approach.

Residential areas are important for the underlying surface of a city, and the sponge construction of a residential area is a key topic in sponge city construction. Taking the Zi-Jing community as the research case, the SWMM model was established for simulation, and the rainwater runoff control, collection, and utilization were compared and analyzed before and after the implementation of sponge transformation for the designed rainfall conditions of once in 3, 5, 10, 20, and 50 years. The results showed that the water depth of the four outlet pipes was not a full tube at the first peak time. The full duration time was reduced to 1–5 h at the second peak, and the flow reduction rate at the pipe outlet was between 30% and 100%. The water storage of sub-catchments A1, A3, and A4 increased significantly and continued to increase after the peak rainfall occurred, while that of A2 decreased significantly after the transformation after the transformation. For the whole residential area, the surface runoff decreased by 37–47%, while the surface water storage and infiltration increased by 8–14% and 23–39% respectively after reconstruction. The direct storage volume of rainwater in the four sub-catchment areas was filled at least once above a once in 5 years scenario. The main conclusions were as follows: Sponge transformation in residential areas with 17.46% sunken greenbelt and 40.85% permeable pavement, and the time of the pipe outlet in full status can be shortened by 30–200 min in different rainfall return periods. With the increase in the rainfall return period, the improvement range of the infiltration increased from 23.36% to 39.54%, the improvement range of the storage capacity for rainwater decreased from 14.36% to 8.06%, and the reduction degree of surface runoff increased from 37.73% to 47.43%. The water consumption for flushing is about 30 m3 per day for 1000 people, and the rainwater storage volume of 765 m3 in this study can meet the flushing water demand of 5000 residents in the community for 3–5 days. [ABSTRACT FROM AUTHOR]

The harsh climate and the aging of urban populations have negatively impacted the quality of life of the elderly in severely cold regions. As a result, there is an urgent need to improve environment quality and accurately evaluate outdoor thermal comfort. However, existing studies have paid little attention to older adults living in severely cold climates. This paper aims to fill this gap by studying the elderly population in severely cold cities with high aging rates in China. By combining on-site testing, questionnaire surveys, CFD modeling, parametric computing, and statistical analysis, the study presents an adapted UTCI for elderly people, as well as multi-seasonal prediction models. The results (1) show that the neutral ranges of the UTCI are significantly affected by both climate zones and age groups. Older people are more tolerant to heat but more sensitive to cold. (2) The results also reveal the importance of factors such as air temperature, wind speed, solar radiation temperature, wind direction, relative humidity, and cloud cover in evaluating outdoor thermal comfort. (3) Multi-seasonal thermal comfort models based on neural networks were developed, and empirical studies verified that the model had the highest accuracy in the transitional season and the lowest accuracy in the winter season.

The transition of residential communities to renewable energy sources is one of the first steps for the decarbonization of the energy sector, the reduction of CO2 emissions, and the mitigation of global climate change. This study provides information for the development of a microgrid, supplied by wind and solar energy, which meets the hourly energy demand of a community of 10,000 houses in the North Texas region; hydrogen is used as the energy storage medium. The results are presented for two cases: (a) when the renewable energy sources supply only the electricity demand of the community, and (b) when these sources provide the electricity as well as the heating needs (for space heating and hot water) of the community. The results show that such a community can be decarbonized with combinations of wind and solar installations. The energy storage requirements are between 2.7 m3 per household and 2.2 m3 per household. There is significant dissipation in the storage–regeneration processes—close to 30% of the current annual electricity demand. The entire decarbonization (electricity and heat) of this community will result in approximately 87,500 tons of CO2 emissions avoidance. [ABSTRACT FROM AUTHOR]

This article presents a demand response scheduling model in a residential community using an energy management system aggregator. The aggregator manages a set of resources, including photovoltaic system, energy storage system, thermostatically controllable loads, and electrical vehicles. The solution aims to dynamically control the power demand and distributed energy resources to improve the matching performance between the renewable power generation and the consumption at the community level while trading electricity in both day-ahead and real-time markets to reduce the operational costs in the aggregator. The problem can be formulated as a mixed-integer linear programming problem in which the objective is to minimize the operation and the degradation costs related to the energy storage system and the electric vehicles batteries. To mitigate the uncertainties associated with system operation, a two-level model predictive control (MPC) integrating $Q$ -learning reinforcement learning model is designed to address different time-scale controllers. MPC algorithm allows making decisions for the day-ahead, based on predictions of uncertain parameters, whereas $Q$ -learning algorithm addresses real-time decisions based on real-time data. The problem is solved for various sets of houses. Results demonstrated that houses can gain more benefits when they are operating in the aggregate mode. Note to Practitioners: Residential buildings besides commercial and public sectors are among the building sectors responsible for high-energy consumption. Numerous measures have been considered to construct more energy-efficient buildings, such as implementing new effective insulation materials and increasing the utilization ratio of sunlight. However, there is also a need for practical solutions to reduce the greenhouse gas emissions and avoid power peak from the residential sector. Under this situation, energy management system aggregator (EMSA) offers the opportunity to exploit the flexibility potential of various houses and other available distributed energy resources, promoting their participation in ancillary services and benefiting from rewards and lower energy bills. In this article, an innovative and comprehensive model predictive control-based scheduling optimization that considers uncertainties of renewable resources and weather conditions is formulated. It can be considered as a practical solution in order to optimally control the operation of a residential community. We proposed a curtailable demand response (DR), where customers agree to participate in DR programs defined by the EMSA in response to price changes. [ABSTRACT FROM AUTHOR]

Background: Residential green spaces and the co-inhabiting bird communities provide critical cultural ecosystem services for urban dwellers, which is critical for citizens’ psychological well-being but has largely been overlooked, especially in China. This study attempted to delineate the relationship between human-nature interactions occurring at different scenarios in the health deriving procedure, and identify the key factors influencing urban residents’ psychological well-being and the potential critical human-nature connections within their living spaces. Methods: Bird, plant, and social surveys were sequentially conducted. A model selection procedure based on AICc information was used to find the key factors that are responsible for residents’ psychological well-being, and logistic regression analysis was conducted to figure out the key drivers for the occurrence of critical within-living-space human-nature connections. Results: Most of the respondents are aware of the neighborhood biodiversity and most of them are fond of it, and respondents who prefer fauna over flora have a considerably lower depression degree. Watching birds is the only critical within-living-space nature-related activity that is responsible for residents’ psychological well-being. Both residents’ psychological well-being and bird watching activity were simultaneously affected by nature-related activities within and outside the living spaces, while their preference for different aspects of biodiversity and their capability in perceiving them were also important, despite the difference in exact parameters. In addition, personal attributes such as age, financial income, and physical or mental health status also contribute to psychological well-being. Conclusions: This study successfully bridged citizens’ minds with health products, and the major contribution was the demonstration of a spatial-scale-oriented circuit for people-nature connections, which contributes to the practical application of nature-based therapy.

The Internet of Things (IoT) has the capability to change our lives. In the near future, every device and almost every object will be linked together and connected to the internet. This paper summarizes the work done in developing a Long Range (LoRa) driven home security system for a residential community in a retirement township. Both hardware and software solutions were used to provide emergency alerts. A panic button was developed to generate alarms manually and embedded sensors were implemented to generate alarms automatically. LoRa wireless communication technology was used to connect all hardware devices in the network and to forward all these data to be stored in the server. A mobile application was developed to provide an alternative way of generating manual emergency alerts and to provide a communication channel in the form of a chat room for residents of the same community as well as the security guards. A web application was developed for residential area security management, where administrators can register new residential areas and assign security guards to these residential areas accordingly.

The transition of residential communities to renewable energy sources is one of the first steps for the decarbonization of the energy sector, the reduction of CO2 emissions, and the mitigation of global climate change. This study provides information for the development of a microgrid, supplied by wind and solar energy, which meets the hourly energy demand of a community of 10,000 houses in the North Texas region; hydrogen is used as the energy storage medium. The results are presented for two cases: (a) when the renewable energy sources supply only the electricity demand of the community, and (b) when these sources provide the electricity as well as the heating needs (for space heating and hot water) of the community. The results show that such a community can be decarbonized with combinations of wind and solar installations. The energy storage requirements are between 2.7 m3 per household and 2.2 m3 per household. There is significant dissipation in the storage–regeneration processes—close to 30% of the current annual electricity demand. The entire decarbonization (electricity and heat) of this community will result in approximately 87,500 tons of CO2 emissions avoidance.

Residential communities are the primary living spaces for people in China. Their associated greenspaces are important elements of the urban ecosystem, providing neighborhood ecosystem services and hosting bird populations of differing community structures compared with other urban green areas. This study analyzes avian community characteristics within residential communities and their differences compared with urban parks in Beijing. Furthermore, we assess the key environmental factors determining species composition and population sizes and the spatial scales at which these factors are most significant. We randomly selected 27 residential communities to assess the habitat structure and landscape characteristics that underpin residential avian assemblages across multiple spatial scales. Independent sample t-test and model selection methods based on AICc were used for data analysis. Eurasian Tree Sparrow (Passer montanus) (hereafter ETS) alone accounted for 91.0% of total individuals. Compared with urban parks, more migrants and omnivores were found in residential communities. Within-site forest patch connectivity simultaneously decided resident species diversity and individual abundance, with the exception of ETS. Tree species richness and coverage are key predictors for resident species richness and ETS numbers, respectively. Even in the most urbanized species, ETS retained ecological attributes found in “wilder” settings. Residential communities skewed toward avian communities with dominant numbers of ETS. In these low-quality habitats, our results suggest that landscape configuration is more important than local habitat structure. Within-site habitat connectivity and the number of surrounding forest patches with shorter between-patch distances deserve more attention in future landscape design for residential communities.

Pedestrian-car interweaving is a prominent problem in old residential communities in Chinese cities. To achieve a better pedestrian-car separation to create a safe and comfortable living environment in old residential communities, this paper investigated the mechanism of the flows of pedestrians and cars on a road network inside an old residential community. A method for calculating the flows of pedestrians and cars was proposed to identify the road segments or nodes where the pedestrian flows are interlaced or intersected with the vehicle flows. This method was applied to the estimation of the traffic in the Wangyuehu Community of Changsha City, China. The estimated distribution of community network traffic and pedestrian-car interweaving sites was consistent with the actual situation. [ABSTRACT FROM AUTHOR]

A simulation method of stochastic heating behaviors in residential communities is developed, which is helpful to accurately predict regional dynamic electricity loads. In this method, the corresponding relationship among the structure of family members, the ownership and the locations of heat pumps should be established for each family firstly. The residents need be divided to several types based on the age, and the occupancy profile and the rules for heating behavior by each type of residents should be set up, as well as their interactive features. A simulation model of stochastic heating behavior in residential communities is established by agent-based modeling. A case study to simulate the stochastic heating behavior in a residential community was made. The result indicates this method is applicable to simulate stochastic heating behavior in residential communities with good accuracy. [ABSTRACT FROM AUTHOR]

The reduction of urban heat island (UHI) and carbon emission is of great importance for thermal environment of urban residential areas. This paper aimed to establish a method to analyze the effect of different underlying surface layouts on the reduction of heat island intensity and carbon emission. Taking Tianjin, China, as a case study area, a computational fluid dynamics (CFD) model, satellite-based remote sensing retrieval technology, and orthogonal experimental design were performed to investigate and quantify the performance of mitigation strategies (i.e., urban green space or water body, cool/high reflectivity pavement, and reflective roofs and facades) on UHI. The results indicated that urban green space is the most natural and effective mitigation strategy, while the widespread application of cool/high reflectivity materials is suggested as a mitigation strategy for building height more than 10 m above the ground. Application of urban water body is the next preferred strategy for air at 1.5 m pedestrian level. Green roofs, rainwater gardens, and permeable brick pavements are also more feasible in high-density cities for mitigating the UHI effect. The above mitigation and adaptation strategies of community scale can be acted on UHI and as a global urban climate change response. [ABSTRACT FROM AUTHOR]

The implications of intergroup contact for the social integration of rural migrant workers have received limited scholarly attention in China. Drawing on Allport's contact hypothesis and its recent extensions, this study investigates, from the perspective of urban residents, the situational and relational contexts within which intergroup encounters occur. Using the Urban Resident Survey conducted in 2014 across thirteen cities in China, we compare contact between urbanites and migrants in two social spaces: the workplace and the residential community. We also examine whether their associations with intergroup friendships differ for urbanites with kinship ties to migrants. Our findings highlight the complexities of contact, along with its strengths and limitations in bringing diverse groups closer together. Workplace contact is positively associated with intergroup friendships, whereas residential community contact is negatively related to such ties. Furthermore, kinship relations with migrants can undermine positive contacts but do not provide a buffer for negative encounters. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has the capability to change our lives. In the near future, every device and almost every object will be linked together and connected to the internet. This paper summarizes the work done in developing a Long Range (LoRa) driven home security system for a residential community in a retirement township. Both hardware and software solutions were used to provide emergency alerts. A panic button was developed to generate alarms manually and embedded sensors were implemented to generate alarms automatically. LoRa wireless communication technology was used to connect all hardware devices in the network and to forward all these data to be stored in the server. A mobile application was developed to provide an alternative way of generating manual emergency alerts and to provide a communication channel in the form of a chat room for residents of the same community as well as the security guards. A web application was developed for residential area security management, where administrators can register new residential areas and assign security guards to these residential areas accordingly. [ABSTRACT FROM AUTHOR]

• A methodology to analyze the vulnerability of PTSs from the perspective of RC. • The functionality of PTSs is measured by the accessibility of RC. • An algorithm is proposed to calculate the accessibilities of each RC. • The vulnerability of PTSs is analyzed under several daily disruption modes. Public transit systems (PTSs) in a city usually consist of multiple systems with different transit modes, such as bus and subway systems, and the connection relationships among those systems can be described in terms of the geographical proximity of different stations. This paper proposes a methodology to analyze the vulnerability of the PTSs in a city from the perspective of their common functionality of providing residential communities access to critical services, including commuting, medical, commercial and education services. The proposed methodology is built upon a multi-layer network, with each layer describing a particular type of public transit system. This paper applies the proposed methodology to analyze the vulnerability of the bus and subway systems in Wuhan, China, under several types of disruptions, including random vehicle accidents, recurrent traffic jams and a real rainstorm disaster. [ABSTRACT FROM AUTHOR]

The evolution towards smart grids (SGs) is mainly characterized by the integration of renewable energy sources (RESs) throughout the grid. The intermittent nature of these sources necessitated the installation of energy storage systems (ESSs) to improve the efficiency and reliability in the power system. Moreover, the ongoing high price of batteries has encouraged the installation of shared ESSs in residential communities. However, managing the shared ESS and the energy flows in the community is considered a key challenge. In order to handle this issue, we introduce a novel energy management system (EMS), namely E nergy M anagement I n residential CO mmunities with shared storage based on multi-agent systems (EMICO). It finds the optimal energy trading operations between households, as well as the operations of the shared ESS that minimize the total energy losses. We first propose a new cluster-based architecture for the residential community in which we integrate Internet of Energy (IoE) devices to manage energy flows and find the shortest path to transfer energy with minimal loss from a cluster to the other. Then, we model our energy management problem as a constrained optimization problem and we use Lagrange multiplier method to solve it in a centralized way. In order to preserve households' privacy, we propose a decentralized approach based on multi-agent systems (MASs) to solve our problem. We test our approach on real data traces obtained from a set of households located in the United Kingdom. Numerical results show that EMICO outperforms a literature approach in terms of energy losses (up to 35.66% of reduction in energy losses), electricity bill (up to 21.21% cheaper), number of exchanged messages (up to 83.81% less messages exchanged), and length of required cables (up to 95.03% less cables required). [ABSTRACT FROM AUTHOR]