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Towards Smart Cities: A Move for Efficient Energy Management From a Home to Cities Exploiting Clouds
Abstract
The reliable, efficient, sustainable and optimal management of city resources to facilitate the inhabitants defines the Smart City (SC). The resources of every sector of a SC are managed for their efficient utilization. The power sector is the backbone of a SC, which should be well planned in its design and structure to optimize power utilization. The integration of Information and Communication Technology (ICT) with conventional power grid allows two-way communication between supply and demand sides, which is defined as Smart Grid (SG). The intelligent monitoring and control systems for SG optimize the power generation and power consumption on supply and demand sides, respectively. On supply-side, fossil fuel is used to run the power generators to fulfill power demand, which is expensive and also emits Carbon-dioxide (CO2) in the environment. High power demand requires more generation as a result more CO2 is released in the environment, which causes the greenhouse effect. Optimized energy demand (power management on demand-side) ensures the optimized power production, which reduces the energy cost and emission of CO2. The demand-side is divided into industrial, commercial and residential sectors. The energy management programs optimize the power demand for these sectors. The industrial and commercial sectors are rigid for their energy demand due to their business portfolio; however, the residential sector is flexible. A energy management program of a home optimizes the energy demand by shifting its load demand of from on-peak to off-time time-slots. This optimization reduces energy cost of the home and power production on supply-side. Moreover, the integration of Renewable Energy Sources (RESs) on demand-side mitigates power demand from the utility (supply-side). The residential sector is further classified into islanded Smart Homes(SHs) and
smart community for energy management. In an islanded SH, the load is shifted from on-peak to off-peak time to reduce power consumption cost while avoiding the peak demand for the supply-side. However, when multiple SHs in a community shift load to avoid peak demand, it may generate a rebound peak and the problem of inefficient power demand persists. So, a global solution is required to be proposed for a smart community by considering power sources and power demand.
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The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community’s and fog’s power demand is fulfilled from the utility. In second scenario,community’s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog’s MG, community’s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog’s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog’s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog’s resource management, measuring processing, and response time are performed in CloudAnalyst.
Cloud computing is the big boom technology in IT industry infrastructure. Many people are moving to cloud computing because of dynamic allocation of resources and reduction in cost. Cloud computing delivers infrastructure, software, and platforms as a service to all consumers. But still, it has numerous issues related to performance unpredictability, resource sharing, security, storage capacity, availability of resources on each requirement, data confidentiality and many more. Load balancing and service brokering are the two main key areas, which ensures reliability, scalability, minimize response time, maximize throughput and cost in the cloud environment. These are the main things we have to focus to improve the performance of the computation. This survey paper presents a comparative and comprehensive study of various load balancing algorithm used in the load balancer and brokering policy used for each service and their scheduling types. The objectives of this survey is to (1) Determine, illustrate, compare and analyze newer methods developed for load balancing and service brokering (the most notable problem) by systematically reviewing papers from the year 2015 to 2018; (2) Classify and analyze techniques based on the key parameters in cloud computing techniques; (3) Ultimately set an updated, thorough and rigorous discussion on load balancing and service broker techniques so as to motivate and direct with valuable references for future research development and direction.
In Japan, residents of apartments are generally contracted to receive low voltage electricity from electric utilities. In recent years, there has been an increasing number of high voltage batch power receiving contracts for condominiums. In this research, a high voltage batch receiving contractor introduces a demand–response in a low voltage power receiving contract, which maximizes the profit of a high voltage batch receiving contractor and minimizes the electricity charge of residents by utilizing battery storage, electric vehicles (EV), and heat pumps. A multi-objective optimization algorithm calculates a Pareto solution for the relationship between two objective trade-offs in the MATLAB ® environment.
The fuel cell (FC) is accepted as the state-of-the-art instrument for reducing CO2 emissions in the field of power generation in Korea. Therefore, the Korean government has planned to expand FC power generation from 565 GWh in 2013 to 13,449 GWh by 2027. This article aims to assess the public value or acceptability of this expansion in terms of reducing CO2 emissions. To this end, we derive the public's additional willingness to pay (WTP) for the expansion using a contingent valuation survey of 1000 Korean households. For the purpose of mitigating the response effect in eliciting their WTP and increasing statistical efficiency in analyzing the WTP data, we employ a one-and-one-half-bounded dichotomous choice question format. Furthermore, we use the spike model so as to model zero WTP responses. The mean additional monthly WTP for the expansion is computed to be KRW 1407 (USD 1.37) per household. Expanding the value to the national population gives us KRW 310.5 billion (USD 304.4 million) per year. We can conclude that Korean households are ready to shoulder some of the financial burden of expanding FC power generation to reduce CO2 emissions.
As a result of the tech advancements, which have not been realized, IT segments, viz. smart transportation and information technology modern smart grid (SG) frameworks, are incorporated with intelligent entities and devices. This form of infrastructure, when implemented in the Internet of Things (IoTs), including the sensor networks, creates a space of online and active objects. The ancient cloud involvement results in meeting more computational and analytical advancements that are decentralized and dynamically consume the resourceful SG environment. This research categorically analyzes the measure through which cloud computing facilities can effectively accomplish the vision and crucial necessities of SG environments, and the services and subdomain calls for fog-centered computing models. The main rationale of this research is to evaluate the capabilities of the fog computing algorithm in effectively interplaying with the foundational positioned cloud computing sustenance, which enables the introduction of novel breeds of latency and actual-time free SG network services. This research also considers the problems and thrusts illustrated over the viabilities of the fog computing for effective SG change.
This paper presents an autonomous model predictive controlled smart photovoltaic (PV) inverter with proactive grid fault-ride through capability. The proposed smart inverter control features decoupled active and reactive power. It can seamlessly switch between the anticipated modes of operation based on grid condition or grid operator command. The smart inverter autonomously adjusts its active and reactive power set-points according to the grid condition, it operates in maximum power point tracking (MPPT) and low voltage ride through (LVRT) modes in normal grid and faulty grid condition modes, respectively. The proposed novel autonomous model predictive control (AMPC) scheme is leveraged to enhance the operation of the smart inverter. The AMPC includes online weight factor auto-tuning and control objective normalization to eliminate the required trial-and-error weight factor design stage in conventional model predictive control. This feature is particularly beneficial to the dual-mode smart inverter operation. The performance of the proposed grid-tied smart inverter based on the AMPC is verified experimentally. The results demonstrate that the proposed AMPC-based smart inverter features robust grid fault detection, autonomous adjustment of active and reactive power set-points, seamless transition between modes of operation, and elimination of controller tuning effort.
We investigate the interaction between the regulated and merchant storage investment made by the social planner and a merchant, through a Stackelberg competition model. In the upper level, the merchant storage owner maximizes its profit through storage investment decisions. Given the merchant storage investment, the social planner makes its own regulated storage investment, overall storage operation, and economic dispatch decisions to minimize the overall system cost in the lower level problem. Both the merchant and regulated energy storage is remunerated based on the inter-temporal difference in locational marginal prices (solved by the lower level problem). We show that the regulated storage profit (of the social planner) is always nonnegative if the storage investment cost is convex in the installed storage capacity with no fixed cost. In particular, the regulated storage profit must be zero (non-positive) if the storage investment cost is linear (affine, respectively) in the installed storage capacity. Numerical results (on the IEEE 57-bus test system) with real-world load data from the California Independent System Operator (CAISO) show that Stackelberg competition between merchant and regulated storage investment can reduce 2–3% of social cost compared to a market without storage. We also show that the merchant (profit-maximizing) investment can significantly reduce the social planner’s expense in storage investment, at the cost of only 0.5% increase in social cost (compared to the socially optimal investment). These results suggest that the government shall encourage merchant companies to invest on energy storage and to participate in wholesale electricity markets.
Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework.