نشریه فناوری های نوین مهندسی برق در سیستم انرژی سبز
سال دوم شماره 1 (پیاپی 5، بهار 1402)

In recent years, the rate of occurrence of natural disasters has increased, which has led to extensive damage to the power system and extensive blackouts. preventive measures can be used in the distribution network to reduce the effects of severe natural disasters. preventive actions are opposite to reactive actions. preventive measures are taken before the incident and reactive measures are taken after the incident. In this paper, a mathematical model is presented to show the effects of preventive actions. In the proposed model, as soon as the accident is predicted, by predicting the exit of the damaged lines in the network using the Monte Carlo method, post-accident failure scenarios are generated. Then, in order to reduce the volume of calculations, scenario reduction is done using Gams. In the last stage, by implementing the proposed model, the optimal location for the installation of portable distributed generation sources and the repair team is determined. The simulation on different case studies shows that using proposed method results in considerable reduction of the energy not supplied (ENS) and the time of power outage for loads, which shows the good performance of the proposed method in facing to future disaster.

In this paper, the different protection challenges of active distribution networks are reviewed and the conventional and non-conventional schemes are examined. In active distribution networks, due to the presence of distributed generations at different levels of distribution network, the functionality of the conventional protection strategies are partially or totally are affected. Therefore, the protection strategies should be updated, and the conventional protective schemes and characteristics should be changed. In this paper, first, the potential protection issues raised of active distribution networks are reviewed. Among the challenges, the bidirectional flow of the fault current, the increased amplitude of fault current, the dependency of the fault current on the operating point, the reduction of reach of the relays, the blinding of the protective relay, unwanted islanding, and etc. are reviewed. Then, the performance of the conventional protections including fuses, overcurrent relays, reclosers under such conditions has been investigated. Furthermore, the existing modified protection methods in the literature are examined, which are classified into two general categories of (i) protective relays with unconventional characteristics, and (ii) adaptive protective relays. Finally, the studied different methods are compared with each other, and their performance characteristics are evaluated.

The presence of distributed generation sources (DGs) will affect the extent and duration of system failures. This will inevitably require the need to review the functioning of the system of protection and re-coordination of the relevant equipment. Because the lack of an appropriate coordination between the DG units connected to a distribution system could lead to the exchange of power between these units and the system's instability, improper operation of the system of protection or the unintended islanding of a part of the system. Due to the fact that synchronous machine type DGs (SMDGs) are the most problematic for system coordination (over-current relays) than other types of DGs; in this paper, controlling the output current of generators Synchronization is proposed by tap changer of the transformer connected to it. In this method, the SMDG's participation in the flow of error is limited by the control of the pincer. Before the fault occurred and in accordance with the system conditions, the transformer tapping device connected to the synchronous generator has been changed and it reduces the flow during an error. This strategy, in addition to reducing the generation error of the generators and transformer assembly installed as a dispersed generation source, also increases the permeability coefficient of these sources. The proposed technique is implemented on a sample distribution system in the ETAP software; the results of the simulation show the effectiveness of the proposed method.

Following the expansion of the use of multi-carrier energy hubs in industries, this paper presents a comprehensive stochastic framework for optimal management and daily scheduling of an energy hub integrated with renewable energy sources and responsive cooling, thermal and electrical loads, and ice storage system. To solve this challenge, the 2m+1 Point Estimation Method (PEM) is used to accurately evaluate the system's uncertainties with low computational complexity. The 2m+1 PEM is a fast uncertainty analysis method based on the Taylor series. This method considers the uncertainty of renewable energy sources, the cooling, electrical and thermal loads, and the exchange price with different upstream energy distribution networks. This paper also presents a new self-adaptive optimization method called the Self-adaptive Modified Slime Mold optimization Algorithm (SMSMA) to solve the complex nonlinear problem of optimal daily scheduling of an energy hub. The improved self-adaptive method is based on the wavelet theory, which improves the capability and ability of the original slime mold algorithm to solve the daily optimal scheduling problem of an integrated energy hub. Numerical results show that the proposed daily stochastic scheduling framework, together with the proposed SMSMA algorithm, effectively reduces the operating costs of energy hubs.

Solar energy is the world's most unique and affordable renewable energy source and can be converted into many other forms. In this article, it will be discussed in a long-term perspective the technical and economic feasibility of installing stand-alone solar power plant units with battery support to supply part of Baghdad's electricity. The objective function of this problem includes the cost of installation and maintenance of solar panels, batteries and inverter, which is solved with a certain interest rate in a 20-year perspective using IPSO and ALPSO methods. Furthermore, the load loss supplied and the charging/discharging limit are among the constraints. This article is unique in that it is implemented in the context of Baghdad city, and it also investigates the possible profit from selling power to main grid. Other features and innovations include the implementation of the new ALPSO algorithm. In this algorithm, the constraints of the problem are respected through a three-step adaptive search process. The results show that the proposed methods significantly reduce the lost load (especially in the ALPSO method), reduce the cost of maintenance and installation, and generally improve the performance of the system.

The economic growth of any country has a lot to do with the infrastructure of the electrical energy supply chain and the ability to access it at low cost. Increasing the resilience of the electric energy supply chain in order to be able to respond to the real time demand of high-consumption and strategic consumers is a challenge that will not be possible without considering long-term demand forecasting and integrated development planning of this chain. This paper presents a long-term demand forecasting approach in the electrical energy supply chain of Isfahan's Espidan iron stone industries. This approach is a combination of wavelet transform, long short-term memory (LSTM) network and finally integrating the results with data-mining technique based on machine learning. The company studied in this research is one of the main suppliers of raw materials in the supply chain of basic metal production industries and one of the ten energy-intensive industries in the electrical energy supply chain of Isfahan province. The only information available from this company is the daily time series signal of the historical electrical energy demand of this industry in a period of 40 months. The data in the studied time series is interrupted so that only 50% of the data has a value and the remaining 50% is zero. This lack of data and the impossibility of access to supplementary data and effective features for forecasting has reduced the density of data and the possibility of long-term demand forecasting faces more problems than continuous time series. The used statistical analysis showed that the annual and seasonal data do not follow the normal distribution and have high distortion and heterogeneity. The proposed method and its results have been compared with other available approaches. The results of 10 iterations of extreme learning machine methods show that the RELM technique with a high confidence level of 95% is more effective than other machine learning methods and has more accurate results.