فصلنامه مهندسی و مدیریت انرژی
سال هفتم شماره 4 (پیاپی 26، زمستان 1396)

Disconnecting microgrid from main grid, may lead to severe frequency changes. Under frequency load shedding method provides a proper protection scheme by shedding certain amount of loads. Under frequency load shedding relay is used for frequency control of islanded microgrid. Each synchronous distributed generator (DG) of microgrid is equipped with a protective islanding detection device such as rate of change of frequency (ROCOF) relay. When microgrid disconnects from main grid DGs may trip by activation of their anti-islanding device. Therefore this paper utilizes ROCOF constraint in order to avoid DGs tripping in an islanding condition. A discretized swing equation is used for modeling the system frequency response. Genetic algorithm is employed to minimize the amount of load to be shed. The proposed method is applied to IEEE-34 bus distribution test system.

Nowadays, Distributed Generation (DG), especially PV systems as a new source of power absorbed a high percentage of investment. Fault Detection and analysis in solar photovoltaic (PV) arrays are important issues to increase reliability, efficiency and safety in PV arrays. Due to PV’s non-linear characteristics, current-limiting nature, high fault impedances, low irradiance conditions, the PV grounding schemes, or inverter condition and protection system weakness, faults in PV arrays are not properly recognized. Therefore, to fill this protection gap, machine learning techniques has been proposed for fault detection based on PV array voltage and current measurements and irradiance and temperature in a grid connected 17.6 kw photovoltaic system.To determine the type and classification of the faults, choosing the best method of classification with high accuracy and finding suitable feature in commercial-scale photovoltaic arrays, are important issues that has not been done so far. The input data for using Bayesian and K-Nearest Neighbor Methods are the simulation results of different defined classes of the line to line and open circuit faults by various temperature and irradiance. The results have shown that using the suggested classification system is very successful in the detection and classification of faults in an array string.

In smart grids paradigm, versatile demand response programs have been devised to accommodate the load flexibilities in optimal scheduling of distribution networks and increasing the techno-economic efficiency of electrification process. The initial models for prioritizing the demand response programs are mainly valid for the case of constant and deterministic load profiles. These models are tackled based on multi-criteria decision making approaches and make use of load’s economic model. This is while, the load demand of end-side consumers are accompanied with uncertainties in their nature. An improper modeling of these uncertainties or excluding of from the developed analytics methods ends in corrupted and unreliable results. To ameliorate the precision of the proposed approach, this study effectively incorporates the load demand uncertainties in prioritizing the demand response programs. The load demand uncertainties are effectively modeled based on normal probability distribution function and represented in the form of adequate number of scenarios. The obtained results are then evaluated based on technique for order preference by similarity to ideal solution (TOPSIS) known as a multi-criteria decision making approach. By this way, the priority of each demand response program is determined. Results demonstrate substantial changes in final ranking of the investigated programs compared to the deterministic load profile in preliminary approaches. This remark approves the significant importance of deploying accurate load demand profiles within prioritizing demand response programs.

Uncertainty analysis of voltage in distribution system is the main feature of current study. This paper presents a probabilistic analysis of radial distribution networks considering wind power resources. The wind turbines are modeled as probability distribution functions. Also, the loads are modeled with Gaussian and gamma distributions. Monte Carlo simulation is used for the probabilistic load flow process. Proposed method is applied to the IEEE 33-bus distribution test system and the results are analyzed. The results study validates the correctness and efficiency of the proposed technique.

In water distribution networks, a significant amount of electricity is used to pump the water into the tanks. Planning and pump schedule could have an important role in energy consumption management and reducing the related cost. The aim of this study is to determine the optimal pump schedule on a 24-hour basis in which aims to minimize the cost of electrical energy, whilst meeting the required time varying consumer with regard to restrictions relating to the minimization of maintenance pumps. For this purpose, the pump schedule optimization problem with pumping water from the wells to the tank is formulated in the form of an Integer Non-Linear Programming model (INLP) and solved by the branch and bound method. In addition to the minimizing the energy consumption cost, the model includes a constraint to limit the number of pumps that are switched to reduce the pumps maintenance costs. To illustrate the effectiveness of the model, the water network of Noosh-Abad city in the outskirts of Kashan city is used as a case study. The results show that the formulated model in this study can meet the both mentioned objectives, so that the outputs of the model compared to the historical operation, both in terms of energy costs and the depreciation of the pumps is in much better situation. In particular, in the case of strict maintenance control, the energy cost is about 15% lower than that of the historical operation.

This research is a numerical investigation about the thermal performance of the floor heating system integrated with metallic bridges. For this goal, aluminum and galvanized metallic bridges with 0.5 mm thickness were embedded within the free space of the pipes. Three different bridge lengths of P/S=2/3, and 1 were studied in this paper in which P and S are the bridge length and free space between two adjacent pipes, respectively. On the other hand, for the floor heating system, two water fluid temperatures of 40◦C and 50◦C were considered. Several results have been obtained in this study. For example, thermal performance of the floor heating system enhances with maximum 12.82% and 6.41% for 40◦C and 12.88% and 6.43% for 50◦C using metallic bridges in comparison to the conventional case (without metallic bridges) in which the rate of heat transfer enhancement of heating panel using the aluminum bridge was 6.05% higher than that of galvanized bridge. Additionally, by increasing the bridge length from to 1, the thermal performance of the floor heating system increases up to maximum 7.04%.