Electric distribution network reconfiguration for power loss reduction based on runner root algorithm

This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem

Enhanced sunflower optimization for placement distributed generation in distribution system

Installation of distribution generation (DG) in the distribution system gains many technical benefits. To obtain more benefits, the location and size of DG must be selected with the appropriate values. This paper presents a method for optimizing location and size of DG in the distribution system based on enhanced sunflower optimization (ESFO) to minimize power loss of the system. In which, based on the operational mechanisms of the original sunflower optimization (SFO), a mutation technique is added for updating the best plant. The calculated results on the 33 nodes test system have shown that ESFO has proficiency for determining the best location and size of DG with higher quality than SFO. The compared results with the previous methods have also shown that ESFO outperforms to other methods in term of power loss reduction. As a result, ESFO is a reliable approach for the DG optimization problem

Optimal distribution network configuration using improved backtracking search algorithm

Optimal network configuration is one of the effective approaches for power loss reduction of the distribution network. This paper shows a network reconfiguration method using improved backtracking search algorithm (IBSA). Wherein, IBSA is improved in the process of generating randomly the initialization population. The network reconfiguration method based on IBSA is used to find the optimal network configuration for the 33-node and 69-node systems. The results are compared to the original backtracking search algorithm (BSA), particle swarm optimization (PSO), firefly algorithm (FA) and previous approaches. From the compared results, IBSA can determine the optimal network configuration with higher success rate than BSA, PSO, FA and lower power loss than other previous approaches. As a result, IBSA is an effective approach for finding the optimal network configuration

Wireless Powered Cooperative Relaying using NOMA with Imperfect CSI

The impact of imperfect channel state (CSI) information in an energy harvesting (EH) cooperative non-orthogonal multiple access (NOMA) network, consisting of a source, two users, and an EH relay is investigated in this paper. The relay is not equipped with a fixed power source and acts as a wireless powered node to help signal transmission to the users. Closed-form expressions for the outage probability of both users are derived under imperfect CSI for two different power allocation strategies namely fixed and dynamic power allocation. Monte Carlo simulations are used to numerically evaluate the effect of imperfect CSI. These results confirm the theoretical outage analysis and show that NOMA can outperform orthogonal multiple access even with imperfect CSI.Comment: 6 pages, 6 figures, accepted in IEEE GLOBECOM 2018 NOMA Worksho

Optimal placement of battery energy storage system considering penetration of distributed generations

This paper proposes the optimal problem of location and power of the battery-energy-storage-system (BESS) on the distribution system (DS) considering different penetration levels of distributed generations (DGs). The objective is to minimize electricity cost of the DS in a typical day considering the power limit of DG fed to the DS. Growth optimizer (GO) is first applied to search the BESS’s location and power for each interval of the day. The considered problem and GO method are evaluated on the 18-node DS with two penetrations levels of photovoltaic system and wind turbine. The results demonstrate that the optimal BESS placement significantly reduces electricity cost. Furthermore, the optimal BESS location and power also help to reduce the cut capacity of DGs as their power greater than the load demand. The compared results between GO and particle swarm optimization (PSO) method have shown that GO reaches the better performance than PSO in term the optimal solution and the statistical results. Thus, GO is an effective approach for the BESS placement problem

ASSESSMENT OF THE HEALTH AND ECOLOGICAL RISKS CAUSED BY FUNGICIDES IN CHRYSANTHEMUM CULTIVATION BY ENVIRONMENTAL IMPACT QUOTIENT

This study uses the Environmental Impact Quotient (EIQ) to assess the health and ecological risks caused by fungicides used in chrysanthemum cultivation upstream of Xuan Huong Lake, Da Lat city. Survey results reveal that 134 farmers use 21 fungicides with 18 active ingredients on a total area of 35.2 hectares. In all, 18 fungicides with an EIQ at the level of “unlikely to be hazardous” (EIQ < 25) are used on about 95% of the acreage, and 3 fungicides with an EIQ of “slightly hazardous” (25 < EIQ < 50) are used on the rest of the area. The Field Use EIQ of fungicide was rated very low in only 8.2% of the survey area and moderate in 48%. Areas with high and very high ratings account for 3% and 41%, respectively. Using fungicides according to the instructions can reduce the Field Use EIQ values in cultivated areas by 38% and return areas with high and moderate ratings to a low rating. Therefore, it is necessary to instruct farmers on the safe use of fungicides and to recommend those with low EIQ values for chrysanthemum cultivation

Enabling non-linear energy harvesting in power domain based multiple access in relaying networks: Outage and ergodic capacity performance analysis

The Power Domain-based Multiple Access (PDMA) scheme is considered as one kind of Non-Orthogonal Multiple Access (NOMA) in green communications and can support energy-limited devices by employing wireless power transfer. Such a technique is known as a lifetime-expanding solution for operations in future access policy, especially in the deployment of power-constrained relays for a three-node dual-hop system. In particular, PDMA and energy harvesting are considered as two communication concepts, which are jointly investigated in this paper. However, the dual-hop relaying network system is a popular model assuming an ideal linear energy harvesting circuit, as in recent works, while the practical system situation motivates us to concentrate on another protocol, namely non-linear energy harvesting. As important results, a closed-form formula of outage probability and ergodic capacity is studied under a practical non-linear energy harvesting model. To explore the optimal system performance in terms of outage probability and ergodic capacity, several main parameters including the energy harvesting coefficients, position allocation of each node, power allocation factors, and transmit signal-to-noise ratio (SNR) are jointly considered. To provide insights into the performance, the approximate expressions for the ergodic capacity are given. By matching analytical and Monte Carlo simulations, the correctness of this framework can be examined. With the observation of the simulation results, the figures also show that the performance of energy harvesting-aware PDMA systems under the proposed model can satisfy the requirements in real PDMA applications.Web of Science87art. no. 81

Investigation on energy harvesting enabled device-to-device networks in presence of co-channel interference

Energy harvesting from ambient radio-frequency (RF) sources has been a novel approach for extending the lifetime of wireless networks. In this paper, a cooperative device-to-device (D2D) system with the aid of energy-constrained relay is considered. The relays are assumed to be able to harvest energy from information signal and co-channel interference (CCI) signals broadcasted by nearby traditional cellular users and forward the source’s signal to its desired destination (D2D user) utilizing amplify-andforward (AF) relaying protocol. Time switching protocol (TSR) and power splitting protocol (PSR) are proposed to assist energy harvesting and information processing at the relay. The proposed approaches are applied in a model with three nodes including the source (D2D user), the relay and the destination (D2D user), the system throughput is investigated in terms of the ergodic capacity and the outage capacity, where the analytical results are obtained approximately. Our numerical results verify the our derivations, and also points out the impact of CCI on system performance. Finally, this investigation provide fundamental design guidelines for selecting hardware of energy harvesting circuits that satisfies the requirements of a practical cooperative D2D system

Optimal solutions for fixed head short-term hydrothermal system scheduling problem

In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems