Abstract: Daylight utilization is a key factor in achieving visual and thermal comfort, and energy savings in integrated building design. However, lack of measured data related to this topic has become a major challenge with the increasing need for integrating lighting concepts and simulations in the early stages of design procedures. The current paper deals with the values of daylight illuminance on horizontal and south facing vertical surfaces; the data are estimated using IESNA model and measured values of the horizontal and vertical illuminance, and a regression model with an acceptable linear correlation is obtained. The resultant illuminance frequency curves are useful for estimating daylight availability on south facing surfaces in Tehran. In addition, the relationship between indirect vertical illuminance and the corresponding global horizontal illuminance is analyzed. A simple parametric equation is proposed in order to predict the vertical illumination on a shaded south facing surface. The equation correlates the ratio between the vertical and horizontal illuminance to the solar altitude and is used with another relationship for prediction of the vertical illuminance. Both equations show good agreement, which allows for calculation of indirect vertical illuminance on a south facing surface at any time throughout the year.
Abstract: In this research, STNEP is being studied considering network adequacy and limitation of investment cost by decimal codification genetic algorithm (DCGA). The goal is obtaining the maximum of network adequacy with lowest expansion cost for a specific investment. Finally, the proposed idea is applied to the Garvers 6-bus network. The results show that considering the network adequacy for solution of STNEP problem is caused that among of expansion plans for a determined investment, configuration which has relatively lower expansion cost and higher adequacy is proposed by GA based method. Finally, with respect to the curve of adequacy versus expansion cost it can be said that more optimal configurations for expansion of network are obtained with lower investment costs.
Abstract: For the sensor network to operate successfully, the active nodes should maintain both sensing coverage and network connectivity. Furthermore, scheduling sleep intervals plays critical role for energy efficiency of wireless sensor networks. Traditional methods for sensor scheduling use either sensing coverage or network connectivity, but rarely both. In this paper, we use random scheduling for sensing coverage and then turn on extra sensor nodes, if necessary, for network connectivity. Simulation results have demonstrated that the number of extra nodes that is on with upper bound of around 9%, is small compared to the total number of deployed sensor nodes. Thus energy consumption for switching on extra sensor node is small.
Abstract: Discrete particle swarm optimization (DPSO) is a
powerful stochastic evolutionary algorithm that is used to solve the
large-scale, discrete and nonlinear optimization problems. However,
it has been observed that standard DPSO algorithm has premature
convergence when solving a complex optimization problem like
transmission expansion planning (TEP). To resolve this problem an
advanced discrete particle swarm optimization (ADPSO) is proposed
in this paper. The simulation result shows that optimization of lines
loading in transmission expansion planning with ADPSO is better
than DPSO from precision view point.
Abstract: In this paper, optimal generation expansion planning (GEP) is investigated considering purchase prices, profits of independent power producers (IPPs) and reliability criteria using a new method based on hybrid coded Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In this approach, optimal purchase price of each IPP is obtained by HCGA and reliability criteria are calculated by PSO technique. It should be noted that reliability criteria and the rate of carbon dioxide (CO2) emission have been considered as constraints of the GEP problem. Finally, the proposed method has been tested on the case study system. The results evaluation show that the proposed method can simply obtain optimal purchase prices of IPPs and is a fast method for calculation of reliability criteria in expansion planning. Also, considering the optimal purchase prices and profits of IPPs in generation expansion planning are caused that the expansion costs are decreased and the problem is solved more exactly.
Abstract: Routing security is a major concerned in Wireless
Sensor Network since a large scale of unattended nodes is deployed
in ad hoc fashion with no possibility of a global addressing due to a
limitation of node-s memory and the node have to be self organizing
when the systems require a connection with the other nodes. It
becomes more challenging when the nodes have to act as the router
and tightly constrained on energy and computational capabilities
where any existing security mechanisms are not allowed to be fitted
directly. These reasons thus increasing vulnerabilities to the network
layer particularly and to the whole network, generally. In this paper,
a Dynamic Window Secured Implicit Geographic Forwarding
(DWSIGF) routing is presented where a dynamic time is used for
collection window to collect Clear to Send (CTS) control packet in
order to find an appropriate hoping node. The DWIGF is expected to
minimize a chance to select an attacker as the hoping node that
caused by a blackhole attack that happen because of the CTS rushing
attack, which promise a good network performance with high packet
delivery ratios.
Abstract: The rangelands, as one of the largest dynamic biomes
in the world, have very capabilities. Regulation of greenhouse gases
in the Earth's atmosphere, particularly carbon dioxide as the main
these gases, is one of these cases. The attention to rangeland, as
cheep and reachable resources to sequestrate the carbon dioxide,
increases after the Industrial Revolution. Rangelands comprise the
large parts of Iran as a steppic area. Rudshur (Saveh), as area index of
steppic area, was selected under three sites include long-term
exclosure, medium-term exclosure, and grazable area in order to the
capable of carbon dioxide’s sequestration of dominated species.
Canopy cover’s percentage of two dominated species (Artemisia
sieberi Besser & Stipa barbata Desf) was determined via establishing
of random 1 square meter plot. The sampling of above and below
ground biomass style was obtained by complete random. After
determination of ash percentage in the laboratory; conversion ratio of
plant biomass to organic carbon was calculated by ignition method.
Results of the paired t-test showed that the amount of carbon
sequestration in above ground and underground biomass of Artemisia
sieberi Besser & Stipa barbata Desf is different in three regions. It,
of course, hasn’t any difference between under and surface ground’s
biomass of Artemisia sieberi Besser in long-term exclosure. The
independent t-test results indicate differences between underground
biomass corresponding each other in the studied sites. Carbon
sequestration in the Stipa barbata Desf was totally more than
Artemisia sieberi Besser. Altogether, the average sequestration of the
long-term exclosure was 5.842gr/m², the medium-term exclosure was
4.115gr/m², and grazable area was 5.975gr/m² so that there isn’t
valuable statistical difference in term of total amount of carbon
sequestration to three sites.
Abstract: Transmission network expansion planning (TNEP) is
a basic part of power system planning that determines where, when
and how many new transmission lines should be added to the
network. Up till now, various methods have been presented to solve
the static transmission network expansion planning (STNEP)
problem. But in all of these methods, transmission expansion
planning considering network adequacy restriction has not been
investigated. Thus, in this paper, STNEP problem is being studied
considering network adequacy restriction using discrete particle
swarm optimization (DPSO) algorithm. The goal of this paper is
obtaining a configuration for network expansion with lowest
expansion cost and a specific adequacy. The proposed idea has been
tested on the Garvers network and compared with the decimal
codification genetic algorithm (DCGA). The results show that the
network will possess maximum efficiency economically. Also, it is
shown that precision and convergence speed of the proposed DPSO
based method for the solution of the STNEP problem is more than
DCGA approach.
Abstract: Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e., expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using genetic algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. Finally, adequacy index could be defined and used to compare some designs that have different investment costs and adequacy rates. In this paper, the proposed idea has been tested on the Garvers network. The results show that the network will possess maximum efficiency economically.
Abstract: Transmission network expansion planning (TNEP) is an important component of power system planning that its task is to minimize the network construction and operational cost while satisfying the demand increasing, imposed technical and economic conditions. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, the lines adequacy rate has not been studied after the planning horizon, i.e. when the expanded network misses its adequacy and needs to be expanded again. In this paper, in order to take transmission lines condition after expansion in to account from the line loading view point, the adequacy of transmission network is considered for solution of STNEP problem. To obtain optimal network arrangement, a decimal codification genetic algorithm (DCGA) is being used for minimizing the network construction and operational cost. The effectiveness of the proposed idea is tested on the Garver's six-bus network. The results evaluation reveals that the annual worth of network adequacy has a considerable effect on the network arrangement. In addition, the obtained network, based on the DCGA, has lower investment cost and higher adequacy rate. Thus, the network satisfies the requirements of delivering electric power more safely and reliably to load centers.