Majlesi Journal of Energy Management
Volume:9 Issue: 4, Dec 2020

The development of electric energy consumption, the high cost of installation of large power plants, the development of transmission lines, price jumps, running out of fossil fuels, environmental pollution and competitive space power systems render the use of distributed generation sources with significant growth.Among the benefits of these resources, reducing losses, improving voltage profile and power quality, improving reliability indices and reducing subscribers' interruptions can be mentioned. The important issue regarding DGs is the placement problem and determining the size of these resources. Improper placement of these resources and their inept production, not only does not improve the network performance, but also it may have adverse consequences on the network voltage profile and losses. Therefore, placement and determining the optimal size of DGs is one of the key issues in distribution system development planning from the perspective of distribution network operator which is addressed in this paper. In this paper, placement and size determination of wind turbines in distribution networks is studied to improve reliability indices. The reliability indices considered are System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Average Service Availability Index (ASAI) and ENS. Placement optimization problem and size determination of wind distributed generation sources have been conducted using hunting search (HuS) algorithm, and the results are compared to particle swarm optimization (PSO). The positive effect of wind generators on improving reliability, as well as the efficiency of the proposed method is studied through simulations on 8 bus radial network.

Solar energy is one of the most important sources of renewable energy that has attracted a lot of attention in recent years. Solar energy is abundant: it is much more available than other energy sources. The energy supplied by the earth in one day with solar energy is enough to supply all the energy required by the earth for one day. Solar energy is clean and does not emit greenhouse gases and does not cause any pollution or harmful by-products for nature. The conversion of solar energy into electrical energy is very useful in various fields. Applications such as residential houses, vehicles, spacecraft and aircraft, and marine applications are among the main uses of energy. In this paper, using TSOL software, a solar heating system (including solar water heater, space heating, etc.) is designed and simulated for different time periods such as annual. With TSOL software, the possibility of economic analysis of design and output reports has been investigated.

The effect of various factors on electricity consumption in Iran has caused that according to international statistics, the consumption of the domestic sector is three times the global average. Among these, one of the most important influential factors is social trust. To experimentally test this issue, the authors of this study studied a sample of 400 people in Isfahan by survey method and using a questionnaire. The findings of this study show that social trust does not directly affect electricity consumption and indirectly increases consumption in such a way that lack of social trust causes people to ignore the requests and messages of officials about Proper electricity consumption. At the same time, the very low price of electricity in Iran and the lack of highly efficient electrical appliances in homes largely neutralize the variable effect of social trust.

In the future, centralized and bulky power systems, will give way to decentralized and smaller systems. Power supply in decentralized systems are done by small generation sources. These small sources of generation that are near to consumers are known as distributed generation(DG) sources. The significant penetration of these resources in power systems has advantages such as reducing losses, improving voltage profiles, improving power quality in energy distribution systems. These advantages are achieved when distributed generation sources with optimal generation size are installed in a suitable place in the system. Therefore, in this study, genetic algorithm has been used to determine the optimal location and sizing of these resources. With this optimization, firstly, a reduction in the percentage total harmonics distortion(THD) of voltage and secondly, a reduction in the total real losses of the distribution system under study have been achieved. The results of the proposed algorithm on the IEEE 33-Bus Radial Distribution System are satisfactory. To perform this test, harmonic load flow with the help of MATLAB software has been used.

PI controller is very common in the control of DVRs. However, one disadvantage of this conventional controller is its inability to still working well under a wider range of operating conditions. So, as a solution fuzzy controller is proposed in literature. But, the main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller design is proposed. The resulted controller is composed of Sugeno fuzzy controller with two inputs and one output. According to the error and error rate of the control system and the output data, ANFIS generates the appropriate fuzzy controller. The simulation results have proved that the proposed design method gives reliable powerful fuzzy controller with a minimum number of membership functions. The Dynamic Voltage Restorer (DVR) is fast, flexible and efficient solution to voltage problem. The DVR is designed for protecting the whole plant with loads in the range of some MVA. The DVR can restore the load voltage within few milliseconds. The simulation results are obtained through MATLAB/SIMULINK software.

In a smart distribution network, microgrids can reduce their daily market costs by buying and selling. The issue of buying and selling offers is challenging due to various uncertainties. This paper presents a two-step strategy for proposing the optimal rate of purchase and sale of electricity despite the dependence of electricity and gas prices on the actual day-to-day market for a multi-carrier microgrid. In this model, the microgrid behavior on the amount of electricity/gas purchase/sale, the simultaneous effect of electricity and gas prices, and energy carriers’ interdependence are investigated. The proposed network’s sale and energy purchase in a multi-carrier microgrid are presented as a two-stage model. In the first stage, the microgrid offers hourly energy buying and selling proposals to the distribution system operator, regardless of the uncertainty. Then, in the second stage, taking into account the offers of the day - previously approved, the microgrid operators in the market act momentarily to balance the load. This problem is solved as an integer linear model mixed with GAMS software and the scenario reduction method. Numerical results show the usefulness of the proposed model by comparing scenarios in reducing the operating costs of multi-carrier microgrids.