Search

With the increase in population and the growth of technology, the load demand has increased and major changes in spinning reserve are unavoidable. Short‐term forecasting to hourly predict the required load and spinning reserve is of great importance. All of the power system studies in planning and operation fields are depend on short‐term hourly load forecasting. In this work, the problem of load forecasting and spinning reserve based on deep learning (DL) algorithms and traditional methods is investigated with the help of the proposed information combination system. The proposed method tries to reduce the weaknesses of the stated methods and increase the accuracy of the predicted signal. First, short‐term predicting of load and spinning reserve is performed using a combination of adaptive network‐based fuzzy inference system (ANFIS) and meta‐heuristic algorithms including differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The ANFIS‐PSO is selected as the best ANFIS combination in load and spinning reserve prediction with a lower error criterion than other methods. Also, the long short‐term memory (LSTM) network can provide good accuracy for load and spinning reserve forecasting. Therefore, the combination of ANFIS‐PSO and LSTM is used to reduce the average error and error variance. [ABSTRACT FROM AUTHOR]

Conventional transmission expansion planning (TEP) has evolved with the increasing influence of distributed generations (DGs) and electric vehicles (EVs). This increases uncertainty in power systems. On the other hand, reactive power plays a key role in voltage stability, so modeling reactive power planning in TEP is very important. The amount of reactive power is a determining factor regarding the stability of the network voltage and in case of shortage, occurrence of some events may endanger the voltage stability. This research aims at proposing a hybrid TEP and reactive power planning for the first time. Furthermore, the impact of wind, PV power plants, and EVs, as the common sources of uncertainties, are included. The ultimate goal of HTEP‐RPP is to find the transmission line candidates that meet the reactive power requirements. The simulation case studies on IEEE 24‐ and 118‐ buses confirm that the TEP based on DCPF may lead to some unreliable operating conditions due to violating the voltage constraints. In this case, the HTEP‐RPP results, by applying the imperialist competitive algorithm (ICA), are more comprehensive and effective than the results obtained by gravitational search algorithm (GSA), for the studied problem or even the popular TEP solutions. [ABSTRACT FROM AUTHOR]

Stability studies in distribution smart grids (SGs) are very important due to the increased penetration of distributed energy resource making bidirectional flow of electric power. This paper presents two novel fast voltage stability indices (VSIs) applicable to the radial distribution SGs based on the real-time measured voltage data only, obtained by the SG infrastructures such as smart metering device. Each VSI depends on the system bus voltages and does not need additional calculations such as power flow solution, converting the actual distribution network into two-bus equivalent system etc. In other words, for a radial system with n-node, the voltage stability analysis is carried out by parallel n-microprocessor units independently. The concentrator units can collect and evaluate the computational results and decide about control actions directly or indirectly depending on the system requirements. The accuracy and efficiency of the proposed indicators are tested on a 41-node radial distribution system. Six load models including constant and composite types have been considered, and also SG in various states of operation and four types of distributed generation are analysed. The comparison results demonstrate that the proposed VSIs are fast and effective to identify the most sensitive node of the radial grids to the voltage collapse based on the smart infrastructures. [ABSTRACT FROM AUTHOR]