Efficient Scheduling of Nonpreemptive Appliances for Peak Load Optimization in Smart Grid

Chakraborty, Nilotpal; Mondal, Arijit; Mondal, Samrat

doi:10.1109/tii.2017.2781284

Cited by 23 publications

(9 citation statements)

References 33 publications

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“…In distributed demand response, the supply side broadcasts the information e.g., current load, tariff. Individual consumers optimize energy consumption [18]. However, error messages on the communication network and locally solving the optimization are challenges for efficient power management.…”

Section: Problem Statementmentioning

confidence: 99%

“…The basic steps are described in Algorithm 1. Find N_tasks allocated to vm 6: if VmId=1 then 7: cur_count=0; 8: else 9: cur_count=vm.getCurAssign() 10: end if 11: find (cur_VmState) 12: State=VmState 13: if (cur_count ≤ MaxCount && 14: State.equals (available)) then 15: MaxCount=cur_count; 16: VmId=vm; 17: end if 18: end for 19: Return VmId…”

Section: Rr Algorithmmentioning

confidence: 99%

“…15: end if 16: if (more requests are in queue) then 17: Repeat step 8 to 15 18: end if 19: Return VM with load 3.2.3. PSO Algorithm PSO is inspired from the motion behavior of birds or fish to improve the candidate solution.…”

Section: Throttled Algorithmmentioning

confidence: 99%

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Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

et al. 2018

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The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud-fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

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Section: Problem Statementmentioning

confidence: 99%

Section: Rr Algorithmmentioning

confidence: 99%

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Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

et al. 2018

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“…CPP is best known to reduce peak loads used in demand-side management activity. To minimize peak load consumption, the work in [39] proposes a coordinated load scheduling and controlling algorithm with less computational overhead to schedule controllable loads. However, dispatchable DGs are not considered.…”

Section: Energy Management Using Heuristic Optimization Approachesmentioning

confidence: 99%

“…Coordinated load scheduling and controlling algorithm [39] Minimizes peak load consumption. Dispatchable distributed generators (DGs) are not considered.…”

mentioning

confidence: 99%

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

et al. 2018

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This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.

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Multi-objective control-based home energy management system with smart energy meter

Kumar

2023

Electr Eng

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Efficient Scheduling of Nonpreemptive Appliances for Peak Load Optimization in Smart Grid

Cited by 23 publications

References 33 publications

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

Multi-objective control-based home energy management system with smart energy meter

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