Sustainable Interdependent Networks

MIT is positioned to excel in a next-generation approach to campus sustainability. With an active administration, emphasis on sustainable guidelines in planning campus construction and energy performance, and collaborative fabric of researchers, entrepreneurs, inventors, and community partners, we are poised now to both accelerate our successful performance and provide leadership on the meaning of creating a “sustainable campus”. We have a particularly unique opportunity in doing so to transform the campus into a scalable living laboratory, a test bed for change, one which matches cutting edge sustainable urban strategies emerging from our classrooms with their implementation at the institute level. Our partnerships with the City of Cambridge and metro Boston and regional and global networks provide further opportunity to advance collective intelligence around shared problems that affect our shared resources and shared desire for a more sustainable future. Each critical decision we make on campus is an opportunity to think deeply about how to ensure the growth of human potential while maintaining the integrity of the natural systems that support life on the planet. A commitment to sustainability will enable a process that allows us to grapple with how best to weigh fiscal, environmental and human health impacts. We are now building the capacity to create this process. A core component of a next-generation approach is the creation of a decision-making platform for the campus that is founded on systems thinking and powered by responsive, robust data. The interactions between human health, environmental quality, and of course, fiscal responsibility are increasingly complex. Even routine decisions become complicated when we take into account the interconnectedness of the systems that sustain us. This chapter will provide insight into the development of a next-generation framework for campus sustainability that draws on case examples from the Massachusetts Institute of Technology and Yale, both based in the USA.

A smart city requires synergistic interaction between several functionally interdependent networks like energy, transportation, water, oil, gas, and emergency services to provide on-demand, reliable services to prosumers. The sustainability of smart city can be guaranteed only through ubiquitous communication and decentralized information exchange between optimization and computational models for the operation, visibility, and control of each constituent network. With the city spanning different societies and jurisdictions, the models must also account for challenges like interoperability, security, latency, resiliency, policymaking, and social behavior. Solutions in the current literature address these challenges in each network exclusively, but the interdependency between them is not properly emphasized. The chapter addresses this gap in research by considering smart city networks with special emphasis on energy, communication, data analytics, and transportation. It introduces each of these networks, identifies state of the art in them and explores open challenges for future research. As its key contribution to the literature, the chapter brings out the interdependencies between these networks through realistic examples and scenarios, identifying the critical need to design, develop, and implement solutions that value such dependencies. Thus, the chapter aims to serve as a starting point for researchers entering the domain of smart city and is interested in conducting cross-functional research across its different interdependent networks.

While the education situations develop rapidly currently, the group of college instructors gains more and more attention. The management of the college is macroscopical, while the position of college instructors is distinctive, and a lot of researchers focus on this group. As to understand the feature of this group, the occupational burnout and organizational commitments of college instructors are the two of important factors. In the current system, it is noticed that the position of the college instructors often is marginalized, such as unclear responsibilities and ambiguous duties, which cause the problem that college instructors sense tough during work, high pressured, and self-role confusion. If they hardly get personal accomplishment and achievement, as a result, the level of occupational burnout would rise up; meanwhile, the level of organizational commitments would decline. This position in college could be difficult to stay steady or be constructed well. Generally, it would affect the development of college and university. Therefore, the study and research for the occupational burnout and organizational commitments of college instructors are vital. However, so far most researches on this subject in Guangxi are apt to be theoretical rather than being practical and functional. This study is based on the review of the related literature and combines the conditions of college in Guangxi. Collecting the data by feasible questionnaire tables among more than 100 college instructors who were selected from about eight colleges of Guangxi, then the data was statistically analyzed by SPSS. According to the result, the main conclusion is made; meanwhile, the outlook of this group would be illustrated. Hypotheses in this study are as follows: H1: The level of burnout among individuals differed via distinctions of gender, age, years of working, marital status, and academic category will be discrepant significantly. H2: The degree of organizational commitments among individuals differed via distinctions of gender, age, years of working, marital status, and academic category will be discrepant significantly. H3: Occupational burnout will be negatively associated with organizational commitments. H4: Occupational burnout will be predictable significantly to organizational commitments.

High compute power provided by the many-threaded SIMT model of Graphics Processing Units (GPUs) accompanied with the recent advancements in their programmability has allowed expression of massively parallel computations. Graph processing is one of the applications that expose such parallelism, and hence, candidates GPUs as attractive execution platforms. However, irregularities in large real-world graphs makes effective and scalable utilization of symmetric GPU architecture a challenging task. While degree distribution in graphs extracted from real-world origins is usually power law, GPUs demand homogeneous computation patterns on consecutive data elements. This article summarizes recent research advancements to overcome this challenge. We first overview the main concepts in the field of graph processing on GPUs. Then, we introduce novel graph representations that, unlike conventional storage formats, are a better match for GPUs. We then present a GPU-friendly decomposition scheme that provides balanced thread to task assignment and enhances the scalability and the execution performance. Finally, we discuss a set of techniques that allow scaling the computation over multiple GPUs efficiently.

Networked control systems (NCSs) are created by the integration of advanced communication networks, control systems, and computation techniques. This integration enhances efficiency and reliability at the expense of increased complexity and reduced security. For example, the reliance of NCSs on communication networks exposes these systems to attack vectors targeting generic networks. This chapter is an overview of pervasive NCSs’ applications, recent attacks on NCSs, and attack detection techniques. A mathematical framework for an NCS under common types of attack is presented, i.e., denial of service (DoS), false data injection (FDI), and time delay switch (TDS) attacks. Thereafter, the framework is used to developed an algorithm based on adaptive channel allocation and state estimation techniques to compensate for the destabilizing effects of TDS and FDI attacks simultaneously. Finally, the proposed algorithm is used in a case study to show the effect of injected attacks on different parts of an NCS and the capabilities of the detection algorithms. Simulation results show the algorithm can accurately detect attacks and can overcome the attack effects by adapting the communication channels.

Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks; however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture the evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of real-world social networks is the presence of leaders, i.e., nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method can find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature enables us to detect and track communities over time rapidly efficiently. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks.

A new era of transportation has experienced electrification and undergoes notable changes in the last few decades. The concern about environmental friendly technology carries almost a huge expansion prospect to electric vehicles (EVs). Whereas plug-in hybrid electric vehicles (PHEVs) are recognized as a feasible term in the line of vehicular technology in the smart electric grid to lessen the dependency on fossil fuels and greenhouse gas (GHG) emissions related to conventional vehicles (CVs). The development of vehicle-to-grid (V2G) strategies establishes win–win situations for the PHEV participation without additional infrastructure cost, reduction of generation, operational and PHEV user cost, reduction of environmental pollution. Together with the expansion of the smart grid technologies, the V2G power allocation problems need to be addressed. More originally, this chapter measures substantial, though often overlooked, social barriers to the wider use of PHEVs (a likely precursor to V2G) and implementation of a V2G transition. This chapter has given an idea that the only important barriers facing the greater use of PHEVs and V2G systems are technical. Instead, it provides a broader assessment situating such “technical” barriers alongside more subtle impediments relating to social and cultural values, business practices, and political interests. Thus, this research study recognizes probable socio-technical obstacles towards widespread adoption of V2G in smart grid and governs that if sustainability problems affect consumer decision to adopt V2G to charge their PHEVs. The current study delivers valuable understanding about the perception among technology fanatics associated with knowledge expansion and improved fortified to sort out the numerous alterations among V2G and PHEVs. Finally, the outcomes of this chapter can guide policy makers to implement V2G technology successfully. Moreover, the chapter illuminates the policy implication of such barriers, which emphasizes what policy makers need to achieve towards V2G technology adoption in smart grid environment while integrating electric vehicles engineering with consumer preference.

Plug-in electric vehicle (PEV) has experienced major transformations since the last few decades. The success of smart electric grid with the addition of renewable energy solely depends on the extensive diffusion of PEV for a carbon-free and sustainable transport sector. Current technical studies concerning numerous optimization methods connected to PEV-integrated smart electric grid such as battery charging and control, unit commitment, vehicle-to-grid (V2G), solar and wind energy integration along with demand-side management have proved that vehicle electrification is a fast developing arena of research. Charging optimization of PEV is an emerging field which is gradually being implemented in many charging infrastructures at a global scale. A near-comprehensive understanding of smart charging capability is crucial for large participation of PEV. Only proper charging can ensure PEV users to be free from ‘range anxiety’ and switch into the new revolution of green vehicle with less CO₂ emissions. This chapter discusses on the aspects of bio-inspired computational intelligence (CI)-based optimizations for efficient charging of PEVs. A holistic assessment of significant research works using bio-inspired CI techniques for PEV charging is presented. A summary of future optimization techniques is also discussed, covering cuckoo search (CS), artificial fish swarm algorithm (AFSA), artificial bee colony (ABC), etc., with broad reviews on previous applied techniques and their overall performances for solving various practical problems in the domain of PEV charging. Furthermore, noteworthy shifts in the direction of hybrid and multi-objective CI techniques are also highlighted in this chapter.

Recent developments in communication systems and protocols led to the greater attention of energy industry to future smart grids. As one of the key aspects of smart grids, demand response is a promising tool for increasing the capabilities, efficiency, and reliability of power systems. Demand response programs focus on the flexibility potentials in demand, instead of paying full attention to the efficiency of energy system infrastructures. So far, several utilities around the world have planned to realize potential benefits of flexible demand of their consumers. However, participation in most of the programs is dominated by large industrial consumers. This focus on large consumers is mostly due to the lack of knowledge among small consumers on how to participate in demand response programs as well as their difficulty in manually responding to the utility signals. This is in spite of the key role that residential consumers can play since more than one-third of electricity produced worldwide is consumed by residential sector. In order to achieve potential benefits of the consumers, automated home load management modules have been proposed to optimize operating status of flexible appliances in response to what happens in the system. These modules autonomously operate the appliances just to simplify consumers response to the signals broadcasted by the operators. The autonomous operation of the modules, however, may threaten the network efficiency since negative consequences like peak rebound during periods with lower prices are likely. In order to prevent such consequences, effective coordination frameworks are necessary to coordinate operating status of the appliances belong to different consumers. In this regard, a few coordination frameworks have been proposed in the literature. Some of them are in the centralized fashion wherein individual appliances data and consumers preferences are gathered in a control center where an optimization model is solved to optimize operation of all appliances. These centralized frameworks, although effective in avoiding the consequences, may jeopardize consumers privacy, lead to congestion in communication infrastructures, and result in crash of control center processors. In order to avoid these challenging issues, some distributed approaches have been proposed in the literature. The approaches are based on different theories such as game theory. The main characteristics of the approaches refer to their affordable run time and computational burden as well as to their ability in attaining the global or near-optimal solution. This chapter focuses on the concepts, models, and approaches associated with coordinating demand response potentials by residential consumers. After a brief explanation on the importance of activating demand response by residential consumers, mathematical model for optimal operation management of individual residential consumers is thoroughly presented. Then, some descriptions and observations are provided to highlight the necessity of coordination framework to avoid negative consequences of autonomous responses. A four-consumer system is simulated to demonstrate how autonomous response by consumers may lead to severe peak rebounds. These descriptions are followed by a review on centralized coordination frameworks and the associated challenging issues. Applying the coordination framework to the four-consumer system, the raised peak rebound is significantly alleviated. However, it is demonstrated how complexity of the model in centralized framework grows as the number of consumers increases. Then, two sequential and non-sequential distributed coordination frameworks are presented. The two frameworks are applied to the four-consumer system. Although both of the frameworks are effective in mitigating the peak rebound, the non-sequential framework is faster. Finally, the chapter is concluded by providing a summary over the provided materials.

Roof-mounted photovoltaic (PV) panels are currently one of the most promising sources of renewable energy in urban areas. Yet, the optimized use of these rooftop PV systems requires an estimate of the potential supply. This chapter presents a review of the existing methodologies to explore the suitability of different methods to estimate the solar PV potential at regional and national scale. A thorough potential study for solar PV over rooftops requires the estimation of multiple variables, including (1) the horizontal components of solar radiation (global, diffuse, direct, extraterrestrial radiations) at the location of interest, (2) the shadowing effects over rooftops, (3) the rooftop slope and aspect distributions, and the rooftop shape, (4) the solar radiation over the tilted rooftops, and (5) the available rooftop area for PV installation. The goal of the present chapter is to review different methods for solar rooftop PV potential, independently from each other. A comparison is given based on the regional characteristics, the scale of study, the availability of data, and the level of accuracy. The methods include physical and empirical models, geostatistical methods, constant-value methods, sampling methods, geographic information systems (GIS) and light detection and ranging (LiDAR) -based methods, and finally machine learning methods. We present for each of them the main principle and theoretical background, as well as a literature review of the most significant studies that applied the method in various contexts. We also discuss the main advantages and disadvantages of the methods as well as present which methods are more suitable to estimate the above-mentioned variables.

Transient stabilization of the power system network is of major importance in this era of the deregulated power systems. Static VAR compensator (SVC) proves to be more economical and efficient than its other counterparts. Due to installation cost of SVCs, there is an exigent requirement to optimize the SVC installation that could yield maximum benefit. In this paper, first the contingencies that result in maximum disturbance to the grid are realized and ranked accordingly. The measurement used for this purpose is the voltage profile index. Next, genetic algorithm has been used to find out the rating of the SVC and its location that could mitigate the contingency and minimize the weighted sum of the total power loss in the grid, sum of voltage deviations at all the buses, and the rating of SVC to be installed. The weights of each of the objective function terms are assigned according to their importance for the system. In order to evaluate the effectiveness of the proposed approach, it is applied to two test networks. It has been found out that the obtained SVC location and rating maintained voltage security and minimized power loss in the event of contingency.

Demand response (DR) is one of the most cost-effective elements of residential and small industrial building for the purpose of reducing the cost of energy. Today with broadening of the smart grid, electricity market and especially smart home, using DR can reduce cost and even make profits for consumers. On the other hand, utilizing centralized controls and have bidirectional communications Bi-directional communication between DR aggregators and consumers make many problems such as scalability and privacy violation. In this chapter, we propose a multi-agent method based on a Q-learning algorithm Q-learning algorithm for decentralized control of DR. Q-learning is a model-free reinforcement learning Reinforcement learning technique and a simple way for agents to learn how to act optimally in controlled Markovian domains. With this method, each consumer adapts its bidding and buying strategy over time according to the market outcomes. We consider energy supply for consumers such as small-scale renewable energy generators. We compare the result of the proposed method with a centralized aggregator-based approach that shows the effectiveness of the proposed decentralized DR market Decentralized DR market.

This chapter presents a review of a proposed model for smart grid implementation on the Galapagos Islands. Unique characteristics of the archipelago such as difficult to transport consumables and fuel from the continent, the stress on public services produced by tourism, and the sensible native flora and fauna turn the grid operation in a delicate task. With this background, an exhaustive model for considering the influence in the network of new services (distributed generation, power storage, and electric vehicles) is presented. This model is linked with geographic information systems from where the information is taken about the connection of different elements, and then, using a Simulink environment, all calculations were made. Results of simulations of real feeders with proposed scenarios are presented, and the results and the economic analysis confirm the advantages of introducing new concepts in the traditional grid transforming it on a smart grid, even so when these benefits are easily observable and measurable. Simulations on a large scale are made in the software CYMDIST.