Majlesi Journal of Energy Management
Volume:12 Issue: 3, Sep 2023

The distribution transformer Load forecasting is very essential in the control of future smart grids and an economical interfacing of Distributed Resources (DRs) to distribution networks. A distribution transformer connects DRs to the main grid. Exact distribution transformer load forecasting makes an economical DRs scheduling possible. In this regard, this paper firstly introduces a new Self-Organizing Fuzzy Neural Network (SOFNN). Then, it applies SOFNN to perform a five-minute load forecasting for a real-life distribution transformer in Lorestan Electric Power Distribution Company (LEPDC). Simulation results for active and reactive powers show that the proposed SOFNN outperforms ANFIS.

Frequency deviation and inter-area transmitted power oscillations are under local load disturbances. This is a major concern in performance and control of interconnected power systems. In this paper, in order to minimize frequency deviations and increase production control performance, a structure of automatic control for power system production using thyristor-controlled phase-shifter, superconducting magnetic energy storage, as well as governor backlash nonlinearities effect on load frequency control is presented. The aim of this paper is to compare oscillations’ damping and stability evaluation when superconducting magnetic energy storage (SMES) is used in an area and thyristor-controlled phase-shifter (TCPS) in series with transmission line and SMES in both areas considering governor backlash nonlinearities in an interconnected two-area network. Simulation results reveal that the network with SMES-SMES is better than TCPS-SMES in terms of load frequency control.

This paper presents an economic framework for the virtual power plant (VPP) to participate in joint energy and regulation markets. Differnet electricity consumers and wind turbine are aggregated in the proposed VPP model. VPP persuades its consumers to provide regulation reserve by signing incentive agreement. A scenario tree is applied to model uncertain parameters including energy and regulation prices, wind turbine (WT) production, regulation reserve calling probabilities and consumer behaviors. Two different case studies are presented to demonstrate robustness of scheduling framework.

This paper presents a new combined technique for minimizing the power loss in distribution system by optimal Distributed Generation (DG) installation together with capacitor placement. Sensitivity analysis is used to identify the optimal candidate locations of DGs and capacitor placement. Installing the distributed generation sources and parallel capacitors can substantially improve the performance of the distribution systems and the problem of placement and finding the size of these sources is associated with equal and unequal constraints, in which a solution for minimizing the losses and costs in these systems is sought. In this paper, using the bat algorithm of placement and giving size, the capacitor and DG have been designed in such a way the most appropriate voltage profile in the buses are provided and the costs of system losses are considerably reduced. This study discusses a method for improving the voltage profile and minimizing the losses and costs. The solution is divided into two parts: (1) sensitivity factor of the losses is used for determining the optimal location of the capacitor and DG; and (2) a new algorithm for determining the size of the capacitor and DG for the target buses is used. Proposed method has been tested on IEEE 34-bus radial distribution system and the results obtained are encouraging.

Digital currencies are one of the important and new phenomena in the field of financial technologies. Countries and financial markets have welcomed these currencies due to their facilitating functions in payment. Electricity distribution companies, which play an essential role in the distribution of energy between subscribers, receive the cost of this distribution in the form of electricity bills from subscribers. Payments move towards digitalization more clearly. In this article, the design of digital software for calculating the amount of electricity of subscribers and converting it into digital currencies is discussed, as well as the design of electricity bills in the Blockchain network platform.

In this paper, two basic steps are taken to optimize and manage the energy of the smart home . In the first step, To provide a mathematical model and energy pattern to determine the temperature of the air conditioner thermostat with regard to climate change, so that ultimately the cost of consumed electricity is minimized and the welfare level of the smart home inhabitants will not fall below the definition. For this purpose, the neural network method has been estimated to have an instantaneous price for electricity in the coming days, with external temperature information outside the home and the current price of electricity in recent days. Then, using the PSO algorithm, the thermostat setting temperature is determined to optimize energy consumption and minimize the cost of consumable electricity. In the second step, while extracting the equivalent electric vehicle load and power consumption to charge it, the technical and economical analysis of providing smart home power supply through the storage battery of the electric vehicle is considered, so that according to the instantaneous electricity price calculated in the first step At a time when the cost of purchasing electric power from the electric network is high, the battery will provide electric power to the smart home power. Economic analysis results show savings on the cost of purchasing electrical energy with the proposed idea of this paper.