نشریه مهندسی برق و مهندسی کامپیوتر ایران
سال چهاردهم شماره 3 (پیاپی 40، پاییز 1395)

Distributed Generation (DG) resources can effect a lot on the reliability parameters in industrial microgrids. So, reliability evaluation of industrial microgrids is presented in this paper using a proposed composite index in the presence of DG resources and demand response (DR). This procedure of the reliability assessment is based on sequential Monte Carlo method with respect to the time varying load model. In this paper, wind and photovoltaic generations those are useful renewable generations are used. Since, the output power of these DGs depends on wind speed and solar radiation that are stochastic variables, therefore a number of scenarios have been considered in order to determine the output power per hour for each of them. According to the large number of generated scenarios, scenario reduction method is used based on two conditions that consist of power generation of DGs and load. Here the new composite index represents changes in the SAIFI, SAIDI and EENS indices per each KW of installed DGs. With considering to industrial load growth in the microgrid, a ten-year period is studied and the scheduling is performed in both islanding and grid connected operational modes. The concept of DR is also used in the islanding operational mode. To demonstrate the effectiveness of the proposed method, the approach is applied on a standard IEEE RBTS BUS2 system in the presence of DG resources and the results in different conditions are achieved.

The basic approach of this paper is to improve the operational condition of distribution systems by the simultaneous utilization of system reconfiguration and direct load control programs. A Genetic Algorithm (GA) based algorithm is employed to find the optimal states of switches as well as the optimal incentives of the demand response programs. The concept of price elasticity of demand is utilized to illustrate the changes of electricity consumption pattern as a result of customers’ participation in Demand Response (DR). The objective function of the proposed model is network operation costs. In addition, voltage constraints, lines capacity limits and the related constraints of DR programs are considered in the optimization problem. Finally, the effectiveness of the proposed method in reducing operation costs is shown using the 33-bus distribution network. The simulation results show that the coordination of reconfiguration and DR can reduce the operation costs and load shedding requirements in addition to solving lines’ over loading problems.

In this paper the optimal bidding strategy of virtual power plant (VPP) in a joint market of energy and spinning reserve service, coupled with reactive power market is investigated. The proposed bidding strategy model is non-equilibrium based on security-constrained price-based unit commitment (SCPBUC), which considers the VPP supply-demand balancing and security constraints. The model is a non-convex mixed-integer nonlinear optimization problem with inter-temporal constraints. It is solved by mixed-integer nonlinear programming (MINLP), and the solution is a single optimal bidding profile for each of the energy, spinning reserve, and reactive power markets.

In modern power systems, utilization of renewable energy sources makes some difficulties for the power grid. One of these problems is shortage of electrical power during conditions which renewable energy sources cannot generate electrical power. For example, during shading conditions, the output power of the PV array is negligible. Using SMES (Superconducting Magnetic Energy Storage) systems is one of the applicable solutions which has been proposed to solve mentioned problem. In SMES system, energy is stored at high power inductances and during critical conditions; this power can be delivered to the load. One of the important parts of SMES systems is Multi input - Multi Output (MIMO) DC-DC converter. In this paper, at first step, one topology has been designed for the MIMO DC-DC converter. This topology has remarkable advantages such as fewer electrical elements and better controllability than other topologies. In the second part of this paper, based on multivariable controller strategy, an efficient controller has been designed for the SMES system which can set the output voltages of DC-DC converter at predetermined values. The most important feature of proposed controller is its efficiency at different conditions which some of these conditions make serious problems for conventional controllers. Generally, the objective of this paper is designing one SMES systems with an appropriate controller, which can regulate the output voltages at different conditions.

This paper proposes a new modified adaptive sliding mode controller in order to control the inverters of DGS in the voltage and current (power) control modes in a microgrid. An observer is used to estimate the uncertain parameters in controller design and considering these estimated values, the controller is adapted to new condition. In the power management strategy, one of inverter controls the voltage and the other inverter controls the load current and balances the active power. Due to delays in startup power electronic converter and sliding mode controller, the result of controller implementation with classical controllers does not meet the requirement and so, considering these delays with adaptive controller, the performance will be improved considerably and the reference signal will be tracked with lower steady state error in comparison with classical sliding mode controller. Moreover, this controller reduces the total harmonic distortion and improves the rms and peak value tracking. Implementation of system using DSP/TMS320F28335 as well as MATLAB simulation validates the performance of system in different conditions.

Combination of 3D stacking and network-on-chip (NoC), known as 3D NoC, has several advantages such as reduced propagation delay, chip area and interconnect, and power consumption, and bandwidth increase. Despite these advantages, 3D stacking causes the increased power density per chip area and subsequently increases the chip temperature. Temperature increase causes performance degradation and reliability reduction. Therefore, design of temperature management algorithms is essential for these systems. In this paper, we propose a task migration scheme for thermal management of 3D NoCs. The process of migration destinations for hot spots is an NP-complete problem which can be solved by using heuristic algorithms. To this end, we utilize a simulated annealing method in our algorithm. We consider migration overhead in addition to the temperature of the processing elements in migration destination selection process. Simulation results indicate up to 28 percentage peak temperature reduction, on average, for the benchmark that has the largest number of tasks. The proposed scheme has low migration overhead.

After the restructuring and privatization of the electricity industry, the main purpose of the DISCOs is to increase their income, according to the existing laws. The existence of private distributed generation (DG) units in the distribution network and the possibility to buy additional electricity of DG with a less price than the wholesale market, capacitor installation, payment of energy not supplied to customers, the cost of energy losses and voltage drop problem in the distribution network, has created opportunities and challenges for DISCOs that are looking for adopting a suitable strategy for higher profits. In this paper, as the first scenario, it is depicted that when there is no DG unit in the network the increasing in the DISCO’s income is achievable by optimal allocation of both fixed and switched capacitors. In the second scenario, this is done by determination of the maximum acceptable DG energy sourced electricity purchasing price with a known location, and in the third one, by determination of the purchase price from the DG while the locating has been done by the DISCO. Simulation studies are done on a 20kV 18-bus distribution network in Rasht city, and the results are presented at the end.

Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for ultimate consumers. In this paper a state space model for EH system is proposed. Due to the dynamic behavior loads and the price uncertainties, a Model Predictive Control approach is suggested for optimal performance. The proposed method is studied on a EH that consists of transformer, boiler, CHP, electrical and heat storages considering demand side management. Finally, the simulation results depicts to demonstrate the effectiveness of the proposed method for optimal operation of the EH.