• Energy Research

This paper proposes a hybrid HS–DE algorithm-based optimal node identification and sizing of shunt capacitors with an aim to enhance the performance of the radial distribution network. To enhance the exploitation power of harmony search algorithm (HSA), differential evolution algorithm (DE) is embedded with it. The combination of sensitivity analysis and hybrid HS–DE algorithm is used to trace the optimal solution for the installation of shunt capacitive compensation. A comprehensive objective function is formulated to minimize real power loss, improve the voltage stability and voltage profile of the distribution network. The suitability of the proposed hybrid HS–DE-based approach has been validated using IEEE 69 node radial distribution test network at three discrete load scenarios, and the result outcomes are compared with HSA, DE and other similar methods in the literature. The simulation results reflect the superiority and robustness of the hybrid HS–DE-based approach for solving shunt capacitor planning problem in radial distribution networks.

In this work, an efficient hybrid optimization approach entitled harmony search and particle artificial bee colony algorithm is proposed to deal with the distribution network reconfiguration and solar photovoltaic-based distributed generation and shunt capacitor deployment in power distribution networks to improve the operating performance of power distribution networks. The proposed hybrid algorithm combines the exploration and exploitation capability of both algorithms to achieve optimal results. The optimization problem is formalized which includes distributed generation and shunt capacitor locations, open/close state of switches as discrete decision variables, and the optimum operating point of compensation devices as continuous variables. An efficient spanning tree approach is utilized to track the optimal topology of the network. The validity of the proposed hybrid algorithm in handling the optimal planning problem of the distribution network is assured through eight different operating scenarios at three discrete load levels. The efficiency of the proposed performance enhancement approaches was validated using 69 node and 118 node distribution networks. The obtained results are compared against similar techniques presented in the literature.

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

Display Omitted A novel hybrid HS-PABC algorithm has been proposed by integrating PSO embedded ABC algorithm (PABC) with Harmony search (HS) algorithm to enhance the HS algorithm performance.Integrated approach of optimal network reconfiguration problem along with DG units and shunt capacitors compensation in radial distribution network using the proposed hybrid HS-PABC algorithm.DG unit with real and reactive power injection capability is considered.69 and 118 node RDN is used for validation. This article presents the significance of efficient hybrid heuristic search algorithm(HS-PABC) based on Harmony search algorithm (HSA) and particle artificial bee colony algorithm (PABC) in the context of performance enhancement of distribution network through simultaneous network reconfiguration along with optimal allocation and sizing of distributed generators and shunt capacitors. The premature and slow convergence over multi model fitness landscape is the main limitation in standard HSA. In the proposed hybrid algorithm the harmony memory vector of HSA is intelligently enhanced through PABC algorithm during the optimization process to reach the optimal solution within the search space. In hybrid approach, the exploration ability of HSA and the exploitation ability of PABC algorithm are integrated to blend the potency of both algorithms. The box plot and Wilcoxon rank sum tests are used to show the quality of the solution obtained by hybrid HS-PABC with respect to HSA.The computational results prove the integrated approach of the network reconfiguration problem along with optimal placement and sizing of DG units and shunt capacitors as an efficient approach with respect to power loss reduction and voltage profile enhancement. The results obtained on 69 and 118 node network by hybrid HS-PABC method and the standard HSA reveals the effeciency of the proposed approach which guarantees to achieve global optimal solution with less iteration.

Abstract This paper investigates the performance of a radial system while installing a Distributed Generator (DG) in existing Distribution Network (DN). The investigation has been performed with various types of DG units for different topologies of the DN. The present work uses a Voltage Stability Index (VSI) for identifying the location for installing a DG in the DN. A multi-objective framework is proposed to evaluate the size of the DG to be installed by reducing power loss and deviation in bus voltage. Genetic Algorithm (GA) is used to optimize the size of DG. The proposed method has been tested with different types of DG units on standard systems (IEEE-33 bus and IEEE-69) with different radial topologies of DN.

The article focuses on the optimal planning of distribution static synchronous compensator (DSTATCOM) and distributed generation (DG) to minimize power losses, and simultaneously enhance the voltage profile, and voltage stability at various nodes of the Radial Power Distribution Networks (RPDN). The study involves combinational choices in the use of the DSTATCOM and DG and it relies on heuristic algorithms namely the Enhanced artificial bee colony algorithm (EABC) which has been formulated based on a modification in the searching strategy of the traditional artificial bee colony algorithm (ABC) by integrating the particle swarm optimization (PSO). The proposed optimization model considers the multiobjective function, including the power loss reduction, branch current capacity limit violation, and node voltage deviation. In the proposed approach, the optimal rating of DG and DSTATCOM is estimated under various load steps by using the EABC algorithm. Two different test cases namely base case, and combined installation of DG and DSTATCOM units were considered to analyze the distribution network performance at different load factors. The power loss minimization, voltage profile, and voltage stability improvement of the network before and after the allocation of DG unit and DSTATCOM is depicted. The study examines the effectiveness of the proposed method through a simulation run on 33-node balanced and IEEE 13-node unbalanced RPDN under time varying load demand. Simulation results reveal that the combined installation of DG unit along with DSTATCOM at a suitable location in 33-node and 13-node distribution networks can minimize the power loss by 62% and 43% respectively under the time varying load demand considered along with significant improvement in voltage profile and voltage stability of the network.

Abstract Data centers are large-scale data storage and processing systems. It is made up of a number of servers that must be capable of handling large amount of data. As a result, data centers generate a significant quantity of heat, which must be cooled and kept at an optimal temperature to avoid overheating. To address this problem, thermal analysis of the data center is carried out using numerical methods. The CFD model consists of a micro data center, where conjugate heat transfer effects are studied. A micro data center consists of servers aligned with air gaps alternatively and cooling air is passed between the air gaps to remove heat. In the present work, the design of data center rack is made in such a way that the cold air is in close proximity to servers. The temperature and airflow in the data center are estimated using the model. The air gap is optimally designed for the cooling unit. Temperature distribution of various load configurations is studied. The objective of the study is to find a favorable loading configuration of the micro data center for various loads and effectiveness of distribution of load among the servers.

Abstract This paper attempts to minimize power losses in radial distribution networks and facilitates an enhancement in bus voltage profile by determining optimal locations, optimally sized distributed generators and shunt capacitors by hybrid Harmony Search Algorithm approach. To overcome the drawback of premature and slow convergence of Harmony Search Algorithm (HSA) over multi model fitness landscape, the Particle Artificial Bee Colony algorithm (PABC) is utilized to enhance the harmony memory vector. In the first approach, the formulation echoes the determination of loss sensitivity factor to decide the sensitive nodes and thereafter decides on the optimal rating through the use of hybrid Algorithm. The second approach encircles the role of hybrid Algorithm to search for both the optimal candidate nodes and sizing of compensating devices by significant increase in loss reduction with the former approach. The procedure travels to examine the robustness of the proposed hybrid approach on 33 and 119 node test systems and the result outcomes are compared with the other techniques existing in the literature. The simulation results reveal the efficiency of the proposed hybrid algorithm in obtaining optimal solution for simultaneous placement of distributed generators and shunt capacitors in distribution networks.

Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.