One of the big concerns of big cities is facing with the increase of fossil fuel vehicles in the roads. The cars intensify greenhouse gas emission in civic centers. One approach to reduce the emission is replacing the cars by Plug-In Electric Vehicles (PEVs). In spite of reducing the emission, PEVs have some adverse effect on electric distribution networks. Technical challenges such as load and loss factors are some of the considerable problems. In this paper, in order to overcome the problems, the effect of integration of different energy networks is introduced. Two scenarios are proposed. In first scenario, a parking lot is just supplied by just electric distribution network. In second scenario, the parking lot is supplied by electricity and gas networks. Finally, the significant effect of multi carrier energy networks to supply the vehicles is confirmed. * * * * * * * * * * * * * * * * پرونده مقاله

In this paper, an attempt has been made to introduce a new control strategy including Plug-in Hybrid Electric Vehicle (PHEV) and Diesel engine generator to control the voltage and frequency of autonomous microgrids. The proposed control strategy has multiple advantages over the recent control methods in microgrids. The proposed method applies the primary and secondary frequency control strategy, simultaneously. However the secondary voltage control scheme is obligatory. In present study, Artificial Neural Networks (ANN) has been implemented for training and validating the advanced droop control (ADC). After ADC unit training, the inverter based DG can be installed in complex microgrids consist of several DGs and loads. Simulation results indicate the effectiveness and applicability of proposed method in controlling the voltage and frequency in autonomous microgrids. پرونده مقاله

This research uses a comprehensive method to solve a combinatorial problem of distribution network expansion planning (DNEP) problem. The proposed multi-objective scheme aims to improve power system's accountability and system performance parameters, simultaneously, in the lowest possible costs. The dynamic programming approach is implemented in order to find the optimal sizing, siting and timing of HV/MV substations, feeders and distributed generations. Based on the input data, the results should be closer to the reality. So, the relevant uncertainties must well incorporate in DNEP modeling to achieve the best possible strategy. The most important uncertainties are the load forecasting, market price errors as well as the uncertainties related to the intermittent nature of the output power of renewable energy resources. Given that DNEP is a multi-objective optimization problem including several objective functions such as: cost based function, voltage deviation, voltage stability factor and measuring the amount of produced emission. NSGA-II as an appropriate alternative results several non-dominated solutions where finally fuzzy set theory is used to select the best compromise solution among them. The proposed scheme is applied to 54-bus system distribution network. The comparison study validates the efficiency of suggested method in the presence of distributed generations. پرونده مقاله

With the aim of reducing cost of electricity consumption and peak load reduction, tools requirement for better managing electricity consumption have become inevitable in recent years. Smart home has some equipment which are controllable and this ability is used for increasing comfort and minimizing electricity cost for residence. As a key component of smart home , Electric Vehicle(EV) ,increase the ability of consumers in participating in Demand Response (DR) programs. One of the key issues raised in this aim is to present a way for entry and exit of this equipment to the network in order to reduce electricity cost for customer. So in this article 5 set of appliances and V2H capability of EV together with Time of Use (TOU)-pricing signal based DR strategy are all combined in a single Home Energy Management (HEM) system for the first time. A Mixed-Integer Linear Programming (MILP) is used for this purpose. پرونده مقاله

In this paper, the Gray Wolf Optimization Algorithm (GWO) will be used for planning a hybrid AC/DC distribution network, and results from the economical and technical point of view will be compared to those of conventional methods, such as Genetic Algorithm (GA). In the proposed model, the objective function is to minimize the Net Present Value (NPV) of the optimal solution. The planning will be done for an empty area, which already doesn’t have any distribution network, at the LV level. In the proposed method, the buses and feeders of the network could be AC or DC. Also, there are AC and DC load points and Distributed Generations (DGs) in the planning area. It should be mentioned that in this paper, the uncertainty of load demand and production of DGs has been regarded. Finally, after planning the distribution network as a pure AC and a hybrid AC/DC configuration for a 14-bus test network, NPV, loss of system and the convergence time of the GWO algorithm will be compared to ones from GA, and advantages of the prior will be shown. پرونده مقاله

Electricity utility have long sought to identify and reduce energy fraud as a significant part of non-technical losses (NTL). Generally, to determine customer’s honesty in consumption on-site inspection is vital. Since, inspecting all customers is expensive, utilities look for new ways to reduce inspection’s range to cases with a higher probability of fraud. One way to reduce the scope of inspection is to use machine learning (ML) algorithms to analysis consumption pattern. But, their performance is not satisfactory due to insufficiency of fraudulent customers. In this paper, a new two-stage ML-based model is presented to detect fraud in distribution network. . In the first stage, an Artificial Neural Network (ANN) is trained to model fraudulent customers, which is used to predict theft scenarios for normal consumers to handle data insufficiency. In the second stage, a Support Vector Machine (SVM) classifier is trained to distinguish normal and suspicious consumers. Assessment and comparison of the proposed algorithm to those of conventional models on a real data set with more than 5000 customers shows its high performance. پرونده مقاله

Every year, a large part of the energy produced by power plants is lost on the way to the consumer in the entire power system, and a significant share of these losses is related to the distribution networks. One of the most important factors affecting losses in distribution networks is the existence of load imbalance due to the random consumption of subscribers and the lack of equal distribution of subscribers among different phases of the network. The main challenge in this field is the lack of an effective method to study the effect of load balancing on the losses of a large-scale network. In this regard, this paper studies the effect of load balancing on network loss reduction by applying the concept of clustering and representative feeders. the effectiveness of the proposed method has been proved by the simulations on the low voltage distribution network of Alborz province, and the results show the high efficiency of the proposed method. پرونده مقاله

Design and expansion of distribution systems seems inevitable in view of the need to satisfy the rise in energy consumption in a technical and economical way. Optimal location, sizing and determining the service area of substations is one of the principle problems in expansion of distribution systems. Also uncertainty is one of the important factors that increase risk of exact decision makings. This paper presents a fuzzy multi-objective model for HV/MV substations planning so that uncertainties are modeled using fuzzy numbers (trapezoidal form). The proposed fuzzy model is based on the risk of economic and technical objectives as well as fuzzy values of investment, operation and loss cost of the substations and primary feeders. This model determines the optimal time, location and size of substations using a multi-objective genetic algorithm (NSGA-II). The proposed model is applied on a typical distribution system to assess the efficiency of the approach. پرونده مقاله

The availability of sufficient and economic online capacity to support the network while encountering disturbances and failures leading to supply and demand imbalance has a crucial role in today distribution networks with high share of Distributed Energy Resources (DERs), especially Renewable Energy Resources (RESs). This paper proposes a two-stage decision making framework for the Distribution Management System (DMS) to flexibly optimize the day-ahead schedule of DERs and market participation of distribution networks under uncertainties, imposed by DGs outage and wind generators. The uncertainties are modeled via scenarios and convolved with each other, and then the joint scenario set is applied in the proposed two-stage programming model. Also the role of network constraints on DMS decisions are seen via a linearized AC power flow model and finally the resulted proposed framework is based on mixed-integer-linear programming (MILP) layout solved by CPLEX 12.6. To examine the effectiveness of the proposed framework, it is used for decision making of DERs scheduling and market participation strategy of a test distribution network. پرونده مقاله

Deregulation policy has caused some changes in the concepts of power systems reliability assessment and enhancement. In this paper, generation reliability is considered, and a method for its assessment using intelligent systems is proposed. Also, because of power market and generators’ forced outages stochastic behavior, Monte Carlo Simulation is used for reliability evaluation. Generation reliability merely focuses on interaction between generation complex and load. Therefore, in this research, based on market type and its concentration, reserve margin, and various future times, a Neuro-Fuzzy system is proposed for evaluation of generation reliability which is valid and usable for all kinds of power pool markets. Finally, the proposed method is assessed on IEEE-Reliability Test System with satisfactory results. It will be shown that if market becomes more concentrated or price elasticity of demand increases, reliability will improve پرونده مقاله

Real-time pricing schemes make the customers to feel the energy price volatility and improve their load profiles. However, these schemes have no significant effect on demand-side uncertainty reduction. In this paper, considering smart grid infrastructures and smart building Energy Management System (EMS), a new real-time pricing scheme is presented to reduce the uncertainty of demand-side. In the proposed method, EMS announces its electric demand during each period of next day to the retailer. The price of energy for the pre-specified amounts is day-ahead price, but any deviation from this amount is settled through spot market price will be determined several minutes before the corresponding period by retailer. Numerical results of an illustrative example are implemented to demonstrate how this scheme makes motivation in customers to reduce their demand uncertainties پرونده مقاله

In today’s restructured environment, congestion management plays an essential role in power system operation. Different methods are presented and discussed in this respect for congestion management in short-term and long-term intervals. It is attempted in the present paper to investigate the impact mechanism of FACTS devices and demand response programs together with generation re-dispatch as some facilities from transmission, consumption and generation sides on short-term congestion management of electricity market. For this purpose, Thyristor controlled Series Capacitor (TCSC) representing series FACTS devices and Direct Load Control (DLC) program representing demand response programs in day-ahead power pool market are mathematically modeled and results will be numerically studied and analyzed on the 14-bus IEEE test system. پرونده مقاله

The biggest challenges faced in big cities are greenhouse gas emission and growing energy needs. Efficient utilization of existing infrastructures has a prominent role in response to the challenges. Energy hub approach embraces performance of different energy networks. Energy hub is defined as a super node in electrical system receiving distinctive energy carriers such as gas and electricity in its input, and based on minimum cost decides when and how much of which energy carrier should supply the hub requirements. In this paper, we examine impact of renewable energy resources (wind and solar) and energy storages (electrical and thermal storages) on short term scheduling of energy hub. Effect of the technologies is also investigated on total operation costs of the energy hub in hot and cold climates. Mixed Integer Linear Programming (MILP) model is used for modeling proposed energy hub. CPLEX solver of GAMS software is employed to solve the problem. The results reveal that when and how much of which energy carrier should be exploited to satisfy hub required demands پرونده مقاله