Karar Mahmoud

Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization

An efficient analytical (EA) method is proposed for optimally installing multiple distributed generation (DG) technologies to minimize power loss in distribution systems. Different DG types are considered, and their power factors are optimally calculated. The proposed EA method is also applied to the problem of allocating an optimal mix of different DG types with various generation capabilities. Furthermore, the EA method is integrated with the optimal power flow (OPF) algorithm to develop a new method, EA-OPF which effectively addresses overall system constraints. The proposed methods are tested using 33-bus and 69-bus distribution test systems. The calculated results are validated using the simulation results of the exact optimal solution obtained by an exhaustive OPF algorithm for both distribution test systems. The results show that the performances of the proposed methods are superior to existing methods in terms of computational speed and accuracy.

Impacts of GERD on the Accumulated Sediment in Lake Nubia Using Machine Learning and GIS Techniques

This chapter aims to study and discuss the effect (hypothesis) of constructing the GERD on the deposited sediment amount in the AHDL. To achieve the objective of this chapter; a machine learning approach represented in a regression tree (RTs) model was used and calibrated to simulate the changes in bed levels and water velocities in the study area within AHDL by using the field measured data and GIS analysis for the year 2008 (reference case). Furthermore, a model verification process has been done to ensure the applicability of the applied model using the available field data in the year 2012. The results of the bed levels and velocities during calibration and verification of the model show low values of RMSE % (for calibration 2.90 and 2.57 for bed levels and velocities, respectively, and for calibration 4.66 and 4.98% for bed levels and velocities, respectively) and high R2 (for calibration 0.9975 and 0.9978 for bed levels and velocities, respectively, and for verification 0.9921 and 0.9959 for bed levels and velocities, respectively), indicating that the model was efficiently calibrated and verified. It shows good agreement between the simulated and measured data (by comparisons of simulated longitudinal and cross sections with the measured ones). Thus, this model is considered trustful and reliable to the prediction of sediment and erosion (bed changes) in the study area within AHDL after GERD construction. Accordingly, four of the possible scenarios are performed through the well-calibrated and verified model by reducing the flow quantity and its associated annual sediment rate by 5–10 and 60–65%, respectively. These scenarios are considered as prediction cases after GERD construction. The impact of GERD construction is then studied by comparing some sections along and across the studied lake portion before and after GERD construction (applied scenarios). This impact appeared clearly as a reduction in the amount of the accumulated sediment (decrease in bed levels) accompanied by an increase in erosion amount. Based on the applied scenarios, results showed that the amount of sediment was reduced by 25–27%, 52–55%, 76–81%, and 90–97% in the year 2030, 2040, 2050, and 2060, respectively, compared to the predicted amount of sediment in the year 2020 without GERD operation/construction. As a positive impact of the GERD construction, the lifetime of the upstream AHD reservoir will be prolonged due to the decrease in the amount of the accumulated sediment. This study provides decision-makers with a preliminary knowledge about the impact of GERD operation/construction on AHDL sediment pattern and consequently on Egypt and Sudan. Moreover, the current study opens new windows for future research to investigate the impacts of the different aspects of GERD of AHDL.

Accurate photovoltaic power forecasting models using deep LSTM-RNN

Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.

Optimal integration of DG and capacitors in distribution systems

This paper presents a method for optimally integrating distributed generation (DG) units and capacitors into distribution systems. The objective function of the optimization problem is minimizing real power losses. General analytical expressions are provided for determining the optimal locations and sizes of multiple DG units and capacitors. The proposed method combines the analytical expressions with an optimal power flow (OPF) algorithm. The proposed method is implemented in C++, and optimal integration of DG and capacitors for the 69-bus test system is performed. Several cases are studied, and the results show that the proposed method is efficient for simultaneously integrating multiple DG units and capacitors into distribution systems.

Optimal combination of DG technologies in distribution systems

This paper proposes an efficient method for allocating multiple distributed generation (DG) technologies in distribution systems. The optimal DG sizes, DG locations, and the best combination between different DG technologies are determined. The objective function is to minimize losses in distribution systems. The proposed method is generic since it can solve the optimization problem with different combinations of DG technologies. A direct and fast solution of the DG allocation problem can be obtained using the proposed method without requiring iterative processes. The IEEE 33-bus distribution system is employed to test the proposed method. Different combinations of DG units are studied and optimally allocated. The results show that the proposed method can handle the optimal solution accurately. It is also demonstrated that determining the optimal combination of different DG technologies can contribute positively on loss minimization in distribution systems.

Optimal Siting and Sizing of Distributed Generations

Recently, the penetration of distributed generations (DG) has been obviously increased in electric distribution networks throughout the world. DGs are small scale generators connected near load centers in networks, thereby avoiding losses in transmission systems and releasing system capacity. At present, there are many types of DG, such as wind power, solar power, fuel cell, biomass, micro-turbines, and diesel engines. DG can play an important role in improving the performance of the networks; therefore, allocating DG optimally is one of the most crucial subjects in DG planning. In this chapter, the DG allocation problem is studied, and an efficient method is presented for accurately solving this optimization problem. The proposed method combines between analytical expressions and an optimal power flow (OPF) algorithm to determine the optimal locations, sizes and the best mix of various DG types for minimizing the total real power loss in electric distribution networks. The proposed analytical expressions are general for directly calculating the optimal sizes of any combination of multi-type DG technologies. The optimal power factors of the various units can be analytically computed, thereby contributing positively to loss reduction. The 69-bus test system is used to test the proposed method. The effectiveness of the proposed method is demonstrated for determining the optimal mix of various combinations of different DG types.

A Novel Smart Grid State Estimation Method Based on Neural Networks

The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods.

Adaptive multi-objective optimization for power loss minimization and voltage regulation in distribution systems

The penetration of distributed generation (DG) has recently been increased worldwide. These DG units have excessive effects on voltage profile and losses in distribution systems. In this paper, an optimization model for active distribution systems is constructed with considering voltage deviation index and active losses. An adaptive algorithm is proposed for optimizing the weighting factors of the objective members in real time simulation so that losses are minimized without violating voltage limits. The reactive power generation capability of photovoltaic (PV) technologies, transformer taps, and dispatchable DG units are incorporated in the optimization model. Real-time simulation for the 33-bus distribution system is performed. The results demonstrate the efficiency of the adaptive algorithm for optimizing distribution systems.