Contributions to the Research/Significance of the Research

 Contributions to the Research 

In this research, optimized power consumption, integrating renewable energy, and providing management services to end-users (e.g.; prosumers, consumers, and power companies) with cost efficiency in residential sectors are the focus. The residential sector is divided into two categories considering the real scenarios for cost-efficient power management. In islanded SH, the appliances are scheduled to avoid power consumption during peak pricing time from the utility [84], [85]. The load of SH is shifted from on-peak to off-peak time to reduce power consumption costs. Cost optimization depends on the efficiency of scheduling techniques. The scheduling of appliances to avoid peak demand concerning the pricing signal of an islanded SH is challenging. In the case of communities, the power demand is optimized by integrating RESs-based MGs with the existing systems [86]-[89]. The power-sharing of private MGs and ESSs reduces the energy consumption cost for a community and reduces the power demand from the supply side. Global optimization of energy for a community needs to define policies or contracts. The defined policies are energy management services run on centralized computing infrastructure, e.g.; cloud and fog [90]-[92]. The provision of real-time energy management services requires efficient resource utilization of computing resources, which optimizes PT, RT, and computing costs [33], [93]. The contributions of this research propose various scenarios for efficient power management. In these scenarios types of power sources, behavior of energy demand, capacity of power generation, policies for energy trade and power sharing, and efficient utilization of computing resources to optimize energy cost for end-users are considered. Given the problem statement and subproblems, the contributions of this thesis are enlisted below.

1. In Section 3.2, heuristic techniques for optimized power consumption have been proposed for cost-efficient in a SH. These algorithms have pros and cons when used in different scenarios. In this thesis, the limitations of algorithms are overcome by hybridizing with Elephant Herding Optimization (EHO). The EHO follows the divide and conquer rule, which helps to reduce the complexity of state-of-the-art algorithms and enhance their efficiency. EHO has two main steps. In the first step, it divides the population into multiple clans (groups) and updates their position. In the second step, the best elements (elephants) are selected or the worst elements are removed. A brief introduction to proposed hybrid algorithms is given below.

 • EHO-GA is a hybrid of EHO and Genetic Algorithm (GA). The mutation step of 15 GA increases the search space and it takes a long time to find the near-real optimal solution. The proposed EHO-GA schedules the appliances efficiently. 

• EHO-FA is a hybrid of EHO and Firefly Algorithm (FA). The FA falls in local optima. The proposed EHO-FA is efficient and schedules the SH’s appliances to reduce power consumption costs as compared to EHO, FA, and unscheduled load.

 • EHO-BFA is a hybrid of EHO and Bacterial Foraging Algorithm (BFA). The BFA also falls in local optima. The process of elimination-dispersal is similar to the mutation of GA, which makes the global optima efficient once it comes to this process. The proposed EHO-BFA overcomes this limitation of BFA and schedules home appliances efficiently to reduce power consumption costs. 

• EHO-BPSO is the hybrid of EHO and Binary Particle Swarm Optimization (BPSO). The challenges in the convergence of BPSO are overcome by hybridizing it with EHO. The proposed EHO-BPSO schedules the appliances efficiently with optimized power consumption. 

• The simulations are performed to schedule home appliances with proposed algorithms considering three pricing schemes: Day-Ahead Real Time Pricing (DA-RTP), Inclined Block Rate (IBR), and Critical Peak Pricing (CPP). The simulations are performed for three Operation Time Intervals (OTIs): 10, 30, and 60 minutes. 2. A system model for a rigid community is proposed in Section 3.4, to provide real-time and cost-efficient services to the communities. The effects of size and number of computing resources along with resource allocation techniques are studied in two scenarios. 

• New Service Broker Policy (NSBP) is proposed to route the requests from the end-user layer to the servers in the data center. The NSBP is a hybrid of Optimized Response Time (ORT) and Service Proximity (SP). It enhances the performance of RT for end-users.

 • PSO-SA load balancer is proposed to allocate the requests on VMs in servers. The load balancer is a hybrid of Particle Swarm Optimization (PSO) and Simulated Annealing (SA). 

• The simulations are performed to analyze the performance of the proposed NSBP and PSO-SA. The simulations are performed with combinations of existing and proposed 16 SBPs and load balancers. PSO-SA is efficient (concerning PT and RT) with a maximum load of requests and a combination of NSBP and PSO-SA is cost-efficient. 3. In Section 3.5, unlike the above contribution, a system model has been proposed in which multiple buildings have access to two fogs in the same region for energy management services. For efficient resource utilization following contributions are made.

 • New Dynamic Service Proximity (NDSP) SBP is proposed to route the requests of end-users to servers or Data Centers (DCs) of fogs. The requests are routed to the servers with a balanced load of requests.

 • Modified Honey Bee Colony (MHBC) is proposed to allocate the requests on VMs in servers or DCs efficiently. It balances the load on available VMs efficiently. 

• The simulations are performed for 12 fogs of 6 regions. The computing cost with the proposed NDSP is more efficient as compared to DRL and ORT. 4. In Section 3.6, a system model is proposed in which huge communities have scaled-up fog, named Hight Performance Fog (HPF), to provide real-time energy management services. Simulations are performed to analyze the performance of fog’s computing resources considering the following parameters, techniques, and models.

 • Increasing the number of VMs for every scenario in three scenarios. 

• A linear (First Come First Serve (FCFS)) and a non-linear (Ant Colony Optimization (ACO)) load balancers are implemented for resource allocation. 

• Simulations are performed for cloud-based systems to compare the efficiency of computing resources and service provision with the proposed fog-based model.

 • The simulations are performed for six regions with the “CloudAnalyst” simulator [94]. 

• The energy consumed by computing resources using resource allocation algorithms is evaluated. 5. In Section 3.7, a fog-based Unified Energy Storage System (UESS) is proposed as-a-service for communities. 

• The BESSs of SHs in communities are unified to share surplus energy within the community.

 • Mechanism of multiple agents is proposed to share the information of BESSs and energy demand of SHs, which helps to form UESSs.

 • The simulations are performed to analyze the efficiency of UESS against the scheduling of appliances and BESSs of SHs without sharing. 

• The simulations are performed by varying the number of virtual resources of fog to analyze the effects on PT, RT, and computing cost. 6. A Fog-based Energy Management-as-a-Service (FEMaaS) is proposed in Section 3.8 to promote the integration of renewable energy with existing power systems.

 • Small and large-scale RESs are encouraged to integrate with the existing system. 

• Energy trading policies are proposed to analyze the cost-efficient power utilization for small and large size communities.

 • The sizes of computing resources for optimizing PT, RT, and computing covering the size of the community are analyzed. 

.• Fog-as-a-Virtual Power Plant (FaaVPP) is proposed for a community of prosumers and consumers.

 • The service encourages to integrate RESs with the existing system by incentivizing the prosumers. 

• A linear model is proposed to punish the community with increased electricity bills when fewer prosumers participate in the proposed FaaVPP. It rewards by reducing the bills when a maximum number of prosumers participate.

 • The proposed linear model is proved. 

• The proposed FaaVPP maximizes the profit for service providers.

• The computing efficiency and energy required by computing resources for energy management service are analyzed with the combinations of SBPs and load balancers.

 1.7.1 Significance of the Research

 The experimental contribution to choosing the number and sizes of computing (physical and virtual) resources along with efficient resource allocation techniques (SBPs and load 18 balancers) to provide time and cost-efficient energy management services to communities are given in Sections 3.4, 3.5, and 3.6. In Sections 3.7, 3.8, and 3.9, energy management services (UESS, FEMaaS, and FaaVPP) are proposed for communities on fogs. The specifications of computing resources are chosen according to the size of the community on the bases of understanding from the experimental outcomes in Sections 4.2, 4.3, and 4.4. The simulations and results in Sections 4.5, 4.6, and 4.7 validate the time and cost-efficient services for communities. The communities reduce their power cost with the proposed services. Similarly, in Section 3.2, energy consumption cost is optimized for an islanded SH. The objective of the research is to minimize the total cost (sum of computing and power utilization cost) for end-power users in the SG. The proposed research is significant for consumers, prosumers, and service-providing companies.

 • Efficient load scheduling to minimize power consumption cost for an islanded SH considering the utility as the power source. 

• Cost-efficient power consumption for a community considering multiple power sources (ESSs, RESs, and utility). 

• Efficient power utilization considering multiple power sources to minimize energy costs for prosumers by maximizing their incentives and minimizing power consumption costs.

 • For power management service-providing companies that maximize their profit by minimizing computing costs and efficient utilization of multiple power sources honoring the contracts. 

• The companies promote the participation of prosumers for cost-efficient power management services to maximize their incentives and minimize global costs for communities. 

• The study is also helpful for smart companies to assess the cost of installation of computing resources and maximize profit by efficiently utilizing multiple power sources following the contracts. 1.8 Organization of the Thesis is organized in the following order of chapters, which contains system models, proposed techniques, simulations, and results for optimized power consumption. The contributions of various researchers for cost optimization in the residential sector of the demand side in SG are discussed.

A variety of scenario-based solutions are elaborated for individual and communal SHs. The optimization of power consumption 19 using appliance scheduling techniques for individual SHs connected with utility and MGs for cost optimization is effective. The scenarios and models are proposed for communal power consumption optimization. The centralized and efficient cloud and fog-based system models for optimized power consumption are elaborated. The pros and cons of proposed system models and their techniques are summarized in their respective tables in the chapter. In Chapter 3, system models are proposed for islanded SHs and for communities. Individual or islanded SHs connected with utility schedule their appliances using proposed techniques to reduce power consumption cost. The performances of techniques are also analyzed. However, the power consumption of multiple SHs from a community is optimized by proposing a cloud

fog-based system models, where fogs provide energy optimization services. The performance of fog computing is optimized with proposed techniques for requests routing on potential data centers and load balancing on computing resources. The recurring cost of MGs and fog computation are calculated, which are payable by the community consumers and prosumers. In this chapter, the cost for optimized power consumption by a community and the recurring cost of fog computation are also calculated using the proposed cloud-fog-based system model for the analysis of total cost optimization and validation of the proposed system model. The simulations are performed for proposed system models and their results are explained in detail.

The optimization of power consumption for individual SH connected with SG is performed by scheduling the appliances. However, the effects of power consumption of neighboring or other communal SHs are not considered. In this chapter, results for cloudfog-based system models for communal SHs for the performance of fog computing using proposed techniques are analyzed. Moreover, the recurring cost of computation and optimized power consumption cost are analyzed. In Chapter 5, the conclusion and future work of the thesis are elaborated. The optimization of power consumption for individual SHs compromises the power consumption cost more than the communal SHs using centralized cloud-fog-based system models. The performance of fog computing is optimized for time-efficient energy management for communal SHs. The pictorial hierarchical flow with contributions of the thesis.