Towards Smart Citie# A Move for Efficient Energy Management From a Home to Cities# Exploiting Clouds

 

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 a conventional power grid allows two-way communication between the 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 the supply and demand sides, respectively. On the supply-side, fossil fuel is used to run the power generators to fulfill power demand, which is expensive and also emits Carbon-dioxide (CO₂) into the environment. High power demand requires more generation as a result more CO₂ is released into the environment, which causes the greenhouse effect. Optimized energy demand (power management on demand-side) ensures 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 in their energy demand due to their business portfolio; however, the residential sector is flexible. An energy management program of a home optimizes the energy demand by shifting its load demand from on-peak to off-time time slots. This optimization reduces the energy cost of the home and power production on the 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 communities for energy management. In an islanded SH, the load is shifted from on-peak to off-peak time to reduce power consumption costs 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.In an islanded SH, (shiftable) appliances are scheduled with an intelligent algorithm to optimize power consumption cost. The high pricing from the supply-side advocates the on-peak time and the algorithm shifts load to off-peak time to reduce power consumption cost. The algorithm is installed as a programs on Energy Management Controller (EMC) of a SH, which controls the operations of SH’s appliance. In this research, divide and conquer behavior of Elephant Herding Optimization (EHO) is hybridized with the Genetic Algorithm (GA), Firefly Algorithm (FA), Bacteria Foraging Algorithm (BFA) and Binary Particle Swarm Optimization (BPSO). These four hybridized algorithms efficiently schedule the appliances to reduce the energy cost for a SH as compared to existing algorithms and unscheduled power demand. For a smart community, energy optimization program(s) is(are) installed on a centralized computing platform near or within the community. The program acts as a service that considers various factors simultaneously to optimize power utilization. In this research, fog based energy management services are proposed for smart communities. The efficient utilization of computing resources of fogs provides near-real-time energy management services. Service broker policies and load balancing algorithms are proposed to reduce the Processing Time (PT), computing cost and energy demand to run the services on the fogs with reduce Response Time (RT). Microgrids (MGs) are integrated to reduce energy consumption cost for communities on demand-side, which also reduce the power demand from the utility on supply-side. The MGs generate cheap and environment-friendly power from the RESs. The SHs of communities also have Energy Storage Systems (ESSs). In this research, energy policies are proposed to encourage the integration of RESs and ESSs with the existing system. These policies reduce energy consumption cost for communities by providing opportunities of energy trading, integrating cheap power of RESs and shared ESSs. The policies are programmed to run on the fogs as services. In this research, Linear Program (LP) models are proposed for the policies, which take less PT, computing cost of fogs and respond in near-real-time. Simulations are performed for various scenarios with proposed resource utilization techniques, e.g.; service broker policies and load balancing algorithms. Simulations are also performed for proposed power policies to integrate RESs, ESSs and opportunities for energy trading. Results show that proposed energy management services run efficiently on fogs with reduced PT, computing cost, reduced energy required by computing resources and reduced RT for end-users.

List of Publications

 

 

Journal Publications

 

1.   Bukhsh, Rasool, Muhammad Umar Javed, Aisha Fatima, Nadeem Javaid, Muhammad Shafiq, and Jin-Ghoo Choi. “Cost Efficient Real Time Electricity Management Services for Green Community Using Fog.” Energies 13, no. 12 (2020): 3164. Download

 

2.   Aldegheishem, Abdulaziz, Bukhsh, Rasool, Nabil Alrajeh, and Nadeem Javaid. “FaaVPP: Fog as a virtual power plant service for community energy management.” Future Gen-eration Computer Systems 105 (2020): 675-683. Download

 

3.   Bukhsh, Rasool, Nadeem Javaid, Majid Iqbal Khan, Zahoor Ali Khan, and Imran Usman. ”Cost efficient hybrid techniques for DSM in smart homes.” International Journal of Ad Hoc and Ubiquitous Computing 33, no. 2 (2020): 90-108. Download

 

4.   Bukhsh, Rasool, Nadeem Javaid, Raza Abid Abbasi, Aisha Fatima, Mariam Akbar, Muhammad Khalil Afzal, and Farruh Ishmanov. “An efficient fog as-a-power-economy-sharing service.” IEEE Access 7 (2019): 185012-185027. Download

 

5.  Bukhsh, Rasool, Nadeem Javaid, Sakeena Javaid, Manzoor Ilahi, and Itrat Fatima. “Efficient Resource Allocation for Consumers Power Requests in Cloud-Fog based System.” IJWGS 15, no. 2 (2019) DOI: 10.1504/IJWGS.2019.099562. Download

 

6.  Bukhsh, Rasool, Nadeem Javaid, Zahoor Ali Khan, Farruh Ishmanov, Muhammad Afzal, and Zahid Wadud. “Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid.” Energies 11, no. 12 (2018): 3345. Download

 

7.  Bakhsh, Rasool, Nadeem Javaid, Itrat Fatima, Majid Khan, and Khaled Almejalli. “Towards efficient resource utilization exploiting collaboration between HPF and 5G enabled energy management controllers in smart homes.” Sustainability 10, no. 10 (2018): 3592. Download

1.1    Energy Management

 Electricity generation and distribution are expensive and complex processes. In the conven-tional power grid, power is produced according to demand and transmitted for distribution on the demand-side. High power demands emphasis on high production while most of the power plants run on fossil fuels, which pollute the environment due to the high emission of Carbon-dioxide (CO₂) [1]. Unfortunately, more than 65% of the total produced electricity is wasted during the processes of production, transmission and distribution [2]. About 80% of the World’s population has access to the fossil fuel based electricity; moreover, the tech-nological developments have rapidly increased the electrical and electronic gadgets from personal to corporate usage [3], [4]. In addition, power consumption behaviour by the masses defines the on-peak load and off-peak load on the supply-side. The power plants on the supply-side run the generators on fossil fuels, which emit the CO₂. The huge amount of CO₂ is emitted during the on-peak load time due to the functioning of additional generators to fulfill the demand. The high concentration of the gas pollutes the environment and has greenhouse effects. The efficient energy management optimizes energy consumption on the demand-side to avoid the on-peak-load as well as to minimize carbon emission in the environment. Various optimization solutions for demand-side energy management have been proposed [5]. However, an efficient mechanism for two-way communication between supply and demand-sides guarantees efficient energy consumption management. The integration of Information and Communication Technology (ICT) to provide a communication platform with the traditional power grid makes the Smart Grid (SG).

1.2   Smart Grid

The National Institute of Standards and Technology (NIST) has defined the SG as the integration of power infrastructure with communication and computing services.

The two-way communication and energy controlling enable the new applications and functionalities for businesses [6]. The intelligent integration of electricity networks and actions of end-users by incorporating the latest smart technologies to ensure a sustainable, economic and secure supply of electricity is called SG; a definition by European technology platform (European Commission 2006). The Smart Meters (SMs), smart appliances, Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) are used on the demand-side for intelligent energy usage. However, power production, transmissions and distribution are the vital operations.

performed with advanced ICTs in SG. The ICTs enable the SG to be operated within the time frames using control command defined by international standards, e.g., IEEE-1547 (an IEEE standard defined for control and management of distributed energy sources. Institute of Electrical and Electronics Engineers (IEEE) is a professional organization dedicated for advancement of technology.) The SG provides simultaneous and smart interaction of power operators and consumers to attain the objectives of a safe, efficient and reliable power system [7].

The global electricity demand has increased in recent years and it is expected to increase exponentially by 2050 [8]. The scientists have proposed the integration of ICT and RESs with traditional power grids to control the greenhouse effects due to carbon emission during power production to fulfill the demand. The integration of RESs has increased the complexity of the power system and has become challenging to maintain the stability of distributed power generation at large scale [9], [10]. The SG promises efficient energy utilization by the integration of distributed power sources with stability, maintainability, fault-detection, self-healing, monitoring and controlling with smart decisions. In SG, energy management is divided into supply and DSM. The energy production, transmission and distribution are performed on the supply-side; however, planning, monitoring and scheduling of loads are performed on the demand-side [11]

1.3    Demand Side Management

The smart grid is scalable, which integrates RESs, distributed grids, ESSs and SHs on the demand-side. In SG, Demand Response (DR) ensures optimized power utilization. The DR program motivates power consumers to alter their energy utilization in response to incentives and electricity pricing [12]. The Demand Side Management (DSM) educates the consumers to reduce power demand or schedules their energy demand to avoid on-peak load. Similarly, the consumers are encouraged to optimize their power consumption by providing them financial incentives, which reduces power demand from the supply-side [13]. The power generations is optimized by optimizing energy demand. The stability of the grid is achievable only with balanced demand and supply. During on-peak load, additional generators are turned on to fulfill the demand. The DSM comprises of various strategies to convince the consumers to reduce the energy consumption during peak-load demand, e.g., peak clipping, shifting load, valley filling and strategic energy conservation [14], [15]. In peak clipping strategy,the consumer reduces the power consumption by turning off unnecessary or low priority appliances via direct load control. In load shifting techniques, incentive-based schemes are proposed to encourage the users to shift load from on-peak to off-peak time. Here, the total load demand of consumers remain unchanged. In the valley filling strategy, the load increases during the on-peak time. An effort to reduce energy consumption by modification of services is energy conservation strategy.

The growing electricity demand compels to produce more energy, which causes carbon emission in the environment. The energy management systems for optimized and efficient energy consumption on the demand-side minimize the energy demand. To analyze the demand-side, energy management is divided into industrial, commercial and residential sectors. Commercial buildings like hotels, hospitals, offices and stores are reluctant to participate in energy optimization due to their business and profits [16]. Industries are also reluctant to schedule their energy due to their big profit [17]. Unlike industrial and commercial sectors the residential sector is flexible to reduce energy utilization cost [12], which provides a high opportunity to optimize power consumption [1], [18], [19]. The information shared between the supply-side and the residential sector encourages the consumers (on the demand-side) to use cost-efficient micro-power generators to avoid expensive utility energy. The variety of RESs and ESSs are widely accepted in the residential sector to fulfill cost-efficient energy demands [20]. When power sources of homes swap from utility to RESs or ESSs for cost-efficient energy consumption, it off-loads the utility to shave the peaks of high load demand, which minimizes power production and carbon emission. The Smart Homes (SHs) with smart appliances schedule the operation of appliances to shift the load for cost efficiency [21]. Load optimization techniques are applied to schedule the appliances in SHs in the residential sector for cost-efficient power consumption. However, the consumer’s satisfaction is compromised due to the shifting of appliances’ operations from the desired time. The RESs-based Micro Grids (MGs) provide uninterrupted and cost-efficient power supply. However, RESs based MGs are expensive and difficult to maintain. Moreover, the intermittent nature of power sources makes the system complex [22]. Battery-based ESS (BESS) is resilient and affordable with lesser maintenance than RES. The BESS is a popular power backup solution for residential consumers [23]. Energy storage companies have been developing scenario-based power solutions using storage systems [24]. These power solutions have high flexibility, reliability, and availability. To avoid expensive energy from the utility during on-peak hours, the storage systems provide a promising cost-efficient solution [19]. The DSM with ESS reduces the load on the utility and also minimizes the cost of consumption [25]. The RESs-based energy solution is more beneficial for multiple homes (e.g., community). However, for islanded SHs, BESSs are popular. So, in SG, the residential sector is further divided into islanded and community SHs. The multiple sources on the demand side are efficiently utilized with energy management pro-grams to optimize power cost and energy demand. For example, deciding appropriate power source to fulfill the demand by considering generation capacity, the electricity-demand, mechanism of coordination between supply and demand-sides, etc. to reduce power cost. The design of a platform considering the geographical location, number of participants and distributed power sources with energy management program, reduces energy cost for the participants [26]. These platforms prefer centralized management for power sharing of distributed energy sources. In communities, the power of small scale renewable power generators and locally maintained ESSs is shared. The integration of distributed RESs and ESSs with the existing system requires contract(s) between utility and power users [27], [28].

1.3.1    Islanded Smart Home

In residential area, conventional and SHs in a residential area consume the energy at utility defined pricing. The utility broadcasts the tariff information and conventional homes consume energy at defined rates; however, SHs optimize their power consumption to reduce energy consumption cost. The tariff is devised on load demand. Early load demand optimizes energy consumption in SG [29]. The SHs use the features of ICT to automate and control the smart appliances [30]. The Home Energy Management Controller (HEMC) manages the appliances to consume energy efficiently. It is installed with intelligent programs, which finds optimal, economical and reliable energy optimization solutions with high user comfort at reduced cost and curtailment of the on-peak load. A variety of mathematical and heuristic load scheduling techniques for various scenarios considering single and multiple homes have been proposed [1], [19]. The use of RESs or ESSs or both in islanded SHs also reduce the on-peak load on the supply-side and energy consumption cost. The research [21] proposes system models for cost efficiency with load scheduling for single and multiple homes in the residential area to analyze the reduction of load peaks and minimization of consumers’ cost. The load is optimized by shifting it from on-peak to off-peak time. If all the homes in the area shift their load from on-peak to off-peak time then the peak will be generated in off-peak time. This communal optimization is an inefficient solution.

1.3.2   Community

The load optimization with respect to energy tariff is an efficient solution when neighboring SHs have different solutions in a community. The optimization of energy consumption considering the whole community needs a centralized mechanism of energy optimization, e.g., cloud and fog based energy management [31] and [32]. The development of ICTs provides the platform for the Internet of Things (IoT), which leads to cloud and fog based systems for distributed and centralized environments. The cloud has huge computing resources as compared to fog; however, it suffers from latency issue due to the huge infrastructure [33]. The fog based energy consumption optimization is proposed to overcome the latency issues of cloud-based system [34]. The centralized energy management solutions are proposed according to the type of a smart community. The smart communities are of two types, which are based on the scalability of their size. A smart community in which the number of residents or SHs is constant is called rigid community; on the other hand, a smart community where number of SHs can be increased in future is known as flexible community. In SG, these communities have several challenges like cost-efficient power management with real-time service, resource optimization, computing cost and integration of green energy [35].

1.4    The Cloud Computing

Cloud computing, referred to as “the cloud”, delivers the services on-demand on pay-as-you-use bases. The NIST defines the cloud as, “a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [36]. However, researchers have redefined cloud computing according to their use of services over the Internet. IBM has defined the cloud as the computing service over the Internet on pay-as-you-use bases with three basic services: Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) [37]. The resources of cloud computing are scalable to meet the demand of users. The services are metered to calculate payments of their usage. The services are, usually, self-service for end-users to customize according to their demands. In SaaS, the softwares run on distance computers (the cloud), which are owned by a company to facilitate its users. The end-users’ machines are connected with the cloud via the Internet for software services. It allows accessing the service from anywhere and anytime. Data is safe if the user’s computer crashes. The cloud resources are automatically scaled according to the demand of the user. An instant business startup and many more benefits are there for SaaS on the cloud. The user does not need to have complete hardware with a software computing machine to manage the complete life-cycle of building applications. All the services can be accessed via the Internet from the cloud of PaaS. The user does not need to maintain a personal computing machine and there is no fear of system crash. The corporate companies can access the cloud for infrastructures like servers, networking, storage and other computing resources via IaaS. IaaS provides flexible, scalable and on-demand resources without maintaining and investing in personal hardware resources.

The IBM also defines the types of the cloud depending to their use and kinds of users. For example, public cloud is owned by companies or government agencies to facilitate the public to use the services (e.g., SaaS, PaaS, IaaS) without investing on expensive personal computing machines. The private cloud is owned by an organization to use solely for its own use. The physical systems are usually placed in the premises of the company to manage; however, maintained by a third party or internally. These systems have company-specific services only and they are managed with sophisticated data security. The hybrid cloud is a combination of public and private clouds. The companies access the services of a public cloud to manage their own services.

The cloud has huge resources and provides access to heterogeneous services for a variety of requests from the end-users via the Internet. The long physical distance between cloud resources and the end-power-users is resolved with routers and switches. However, a number of hops between the user and the cloud as well as the huge load of tasks performed on the cloud compromise the Response Time (RT) [38]. In addition, a common pool of data of a variety of users makes it unsecured [39].

1.5    The Fog Computing

The limitations of cloud-based systems are overcome with fog based system. The end-users are directly connected with the fog for efficient energy utilization. Cisco defines fog computing as the extension of cloud computing from the core to the edge of network [40].

The fog resources are placed at the edge of the network and close to the end-users. The end-users are, usually, directly connected with the fog for high computation with minimum RT. The fog has limited computing resources as compared to the cloud. A huge load of requests for services can affect the computational efficiency. In multi-layered system, these requests are offloaded to the cloud to optimize the computation for the service(s) [41]. The end-users’ layer is connected with the fog and the fog is connected with the cloud to access the computing resources. The RT for the end-users’ requests is significantly reduced with the fog as compared to the cloud-based system. Moreover, when a huge load of tasks or requests reach out from the end-users to process, a performance bottleneck is created for the fog due to its limited resources. However, efficient resource utilizing techniques are used to resolve such issues.

Yousefpour etal: [42], reviewed the differences between cloud and fog. In vertical platform architecture, the fog has limited hardware resources, which means fog provides limited computation with lesser power consumption as compared to the cloud. Otherwise, fog is similar to the cloud. In horizontal architecture, the fog and end-users are at first hop; however, in the cloud-based system, there are multiple hops in the network. The network node devices like routers and switches as well as long physical distance between end-users and the physical cloud cause delayed responses. The fog based systems are more suitable for time-sensitive applications as compared to cloud-based systems. A data center has a large number of computing resources (e.g., computer servers) for remote processing, storage and data distribution; etc. The environment or system for accessing and sizes of these resources defines cloud or fog as discussed above. Hence, in this thesis “data center” is used alternatively for fog and cloud.

1.5.1    Communication Network

In smart homes, the home area network is designed for communication of smart appliances with centralized EMC. This communication helps to optimize power consumption with respect to the information of appliances shared with EMC. The architecture design of the network with control programs is implemented for a SH. The network supports both wired and wireless technologies. The wireless media like Bluetooth, WiFi, WiMAX, etc. are commonly used in such networks [43]. A control program with user interface links the smart meter, appliances and consumers. The high bandwidth Internet and proliferation of the IoT have accelerated the deployment of home area network with the static and mobile controlling facility. The IoT devices also encourage to design a network for the distributed and centralized environment for energy management using the cloud and fog based systems. In fog based systems, the end-users are directly connected with the fog using long-range wireless technologies like ZigBee as well as wired technologies like Ethernet for efficient RT [43], [44].

1.5.2   Virtual Machine

Virtual Machine (VM) is a software that mimics the physical machines. Unlike simulator that mimics how a physical machine works, the VM extends the utility by serving and supporting the resources [45]. In fog and the cloud, VMs share the computing resources like storage and processing power of CPU [46]. Each VM acts as an independent resource while residing in the same machine. In fog and cloud, the data centers have physical components for huge storage and processing units, where VMs are created to share these huge resources. The VMs have different sizes and are created or killed according to the static or dynamic scenarios. The performances of the fog and the cloud are affected by the size and number of VMs created. Moreover, the load of requests are assigned to VMs in the data centers of cloud or fog; however, inefficient load assigning compromises the resource utilization. Meta-heuristic techniques are used to balance the load of tasks on the VMs in the data center to enhance the performance. For example, fog data center (or fog) has physical computing resources like high-performance processors, huge memory, etc. These resources are too much for the tasks assigned to them. The resources should be shared for efficient utilization. Hence, VMs are created, which act as independent machines for each type or set of tasks assigned to them for execution. The number of tasks or jobs or requests assigned to a VM is termed as “load”. The efficiency of computing resources is achieved when the load on all VMs is balanced. Hence, an intelligent algorithm balances the load on available VMs and enhance the performance of the fog’s computing resources (or fog data center).

1.5.3    Bio-inspired Heuristic Techniques

There are mathematical, logical and nature inspired solutions for optimization. The meta-heuristic optimization techniques inspired by the nature are efficient and effective. These are inspired from Physics, Chemistry, Biology and computational psychology, etc.; however, bio-inspired optimizing techniques are more adopted by the researchers as compared to the rest of the techniques [47]. Bio-inspired heuristic techniques are categorized into five categories based on evolutionary process, the behavior of collective swarm, ecological processes, human intelligence and the physical phenomenon [48]. In SG, the algorithms inspired from these categories are used for optimized power consumption on the demand-side. In islanded SHs, heuristic techniques are used to optimize the power consumption of the appliances by shifting load from on-peak to off-peak time. In fog and cloud computing, the balanced load of tasks or requests are allocated on VMs in the data centers.

1.5.4    Service Broker Policies

The Service Broker Policies (SBPs) are constraints used to route the requests on potential data centers of the cloud and fog. These data centers are selected according to the rules defined for efficient RT. These rules consider the constraints like nearest data center, load traffic on the network, a load of tasks or requests on the data center, shortest RT, load and RT prediction and proximity. The SBPs are also defined in consideration of data center architecture, design (distributed/centralized), data center energy consumption, static and dynamic load traffic and routing mechanism, etc. [49], [50]. The SBPs and load balancing algorithm manage the task onto the computing resources to overcome the latency issues, which occurred due to Processing Time (PT) and RT. Moreover, efficient resource utilization also reduces the computing cost.

1.6    Problem Statement

The strategy of DSM to shift the load is generally adopted to minimize the cost of power consumption by avoiding the peak pricing using intelligent scheduling techniques [51]. In the literature, a variety of heuristic techniques are proposed to schedule the electric appliances in order to reduce the cost of power consumption. Moreover, different hybridization schemes are available in the literature to explore and exploit the search space to achieve better optimality. However, the complexities of these heuristic algorithms have not been considered to analyze the performance efficiency. The algorithms with high complexity may provide efficient scheduling of appliances; however, execution and time complexities are challenging to tackle [52]. These algorithms are loaded in an energy management controller, which is installed.in a home or building to manage, schedule and control the electric appliances. Moreover, the integration of RESs is being widely promoted across the globe [53]. The surplus energy generated by RESs is shared among the energy deficit users through peer-to-peer energy sharing. In peer-to-peer energy sharing, a local market platform is provided where all the prosumers share power without the involvement of a third party [53]. In distribute envi-ronment, the peer-to-peer power sharing requires an efficient system with minimum delays [54]. Cloud computing provides an efficient energy management platform for centralized and distributed environments [33], [55]. This motivates the companies to use the resources: hardware, storage, applications, etc. without huge investment to purchase, install and main-tain system [56]. The cloud has virtually infinite resources and provides efficient energy management in SG [57]; however, its infrastructure has latency issues [58]. The fog provides services on the edge of the network with the direct connectivity of end-users. It overcomes the latency issues. A huge load of requests or tasks creates the performance bottleneck for fog based system because of limited resources [59]. Efficient resource utilization is required to minimize computing cost, RT, PT and to provide optimal energy management services for communities. In the real-world, renewable energy is integrated under the signed contract between the power utility and prosumers [60]; however, PT increases with the expansion of community size [61]. From the end-user’s aspect, longer latency (sum of PT and RT) can degrade the energy management services. Cost efficient energy management with real time service for communities of a smart city consists of multi-dimensional challenges. To address these challenges sub-problems are defined in following subsections.