Photovoltaic and Wind Turbine Integration Applying Cuckoo Search for Probabilistic Reliable Optimal Placement

This paper presents an efficient Cuckoo Search Optimization technique to improve the reliability of electrical power systems. Various reliability objective indices such as Energy Not Supplied, System Average Interruption Frequency Index, System Average Interruption, and Duration Index are the main indices indicating reliability. The Cuckoo Search Optimization (CSO) technique is applied to optimally place the protection devices, install the distributed generators, and to determine the size of distributed generators in radial feeders for reliability improvement. Distributed generator affects reliability and system power losses and voltage profile. The volatility behaviour for both photovoltaic cells and the wind turbine farms affect the values and the selection of protection devices and distributed generators allocation. To improve reliability, the reconfiguration will take place before installing both protection devices and distributed generators. Assessment of consumer power system reliability is a vital part of distribution system behaviour and development. Distribution system reliability calculation will be relayed on probabilistic reliability indices, which can expect the disruption profile of a distribution system based on the volatility behaviour of added generators and load behaviour. The validity of the anticipated algorithm has been tested using a standard IEEE 69 bus system

Comparison of modern heuristic algorithms for loss reduction in power distribution network equipped with renewable energy resources

This paper presents a comparison between four modern heuristic algorithms for optimal loss reduction of power distribution network equipped with renewable energy resources. These algorithms are Gravitational Search Algorithm (GSA), Bat Algorithm (BA), Imperialist Competitive Algorithm (ICA) and Flower Pollination Algorithm (FPA). Placing Renewable Distributed Generators (RDGs) such as wind turbine (WT) and photovoltaic panels (PV) in the electrical grid might share in reducing the power loss. In this research, the proposed heuristic algorithms are utilized to find the optimal location and size of RDGs on the distribution network for the purpose of reducing power loss. A probabilistic optimal load flow technique is implemented to model the behavior of RDGs based on different penetration levels. The proposed algorithms are applied to 69-bus system. The acquired results based on the heuristic algorithms are listed to clarify the effectiveness of the proposed algorithms in reducing the power losses of the studied system. Keywords: Heuristic algorithms, Power loss reduction, Probabilistic optimal power flow, Renewable energy resource

Application of DSTATCOM coupled with FESS for Power Quality Enhancement and Fault Mitigation

In power systems, the use of renewable energy, especially Wind power generation is steadily increasing around the world. However, this incorporation and the lack of controllability over the wind, and the type of generation used cause problems in the power quality and in the dynamics of the system. In this work, the use of a Distribution Static Synchronous Compensator (DSTATCOM) coupled with a Flywheel Energy Storage System (FESS) is proposed to mitigate problems introduced by the intermittency of wind power generation. A dynamic model of the DSTATCOM/FESS device is briefly presented and a multi-level control technique is proposed. The proposed control technique has one control mode for active power, and two control modes to choose between, for reactive power and voltage control. The above technique has been used here to enhance not only the steady state operation but also to mitigate sudden load changes. The control system under consideration, with the DSTATCOM/FESS, and its controls are analyzed also, under the conditions of different faults which may happen in the system.  Simulation tests of the device are analyzed when it is combined with wind generation in the electric system. The results demonstrate satisfactory performance of the proposed control techniques, as well as a high effectiveness of the control system to mitigate problems introduced by wind power generation

Comparative study of PID controller designs for AVR using different optimization techniques

This paper presents the optimal PID tuning study to improve the dynamic performance of an automatic voltage regulation (AVR) system. The system under study consists of a synchronous generator whose reference voltage changes in a step function and tries to overcome the transient behavior of its terminal voltage smoothly. To optimally control the performance, different optimization techniques are applied to tune the controller gains to obtain the minimum steady state error (main objective) and better dynamic characteristics (rise time, settling time, max overshoot, etc.). Then the AVR system responses with a PID controller based on different optimization techniques are compared to find out which is the best technique

A New Distribution System Performance Approach to the Switch Allocation Problem Under Smart Grid Framework

This paper proposes a new vision of the Distribution System Switch Allocation problem considering new system performance measures. The mathematical model has been rebuilt with a new aggregated multi-objective formula, minimizing a newly developed performance index while achieving minimum annual energy lost. A new practical weighted combined system performance index, consisting of Reliability, Resiliency and Vulnerability, is applied and tested to be used by utilities replacing the common simple reliability index combination. The new model uses mixed integer design variables to determine the number, location and status of switches. A set of eight logical and technical constraints was applied to provide the best description of the real existing system constraints. A new algorithm of checking the system radial topology is also applied to the problem. The problem was solved using the Genetic Algorithm and was tested on a 54-bus real distribution test system, deemed more complicated than the test systems found in literature, to demonstrate its validity and effectiveness in real life systems

The impact of smart transformer on different radialdistribution systems

The work is intended to extend the application of a smart transformer on a radialdistribution system. In this paper, an updated algorithm on the backward/forward powerflow is introduced. The so-called direct approach of power flow is employed and analyzed.In addition, the paper focused on integrating a smart transformer to the network and solvingthe updating network also using the direct approach load flow. The solution of the smarttransformer using the direct approach power flow method is quite straightforward. Thismodel is applied to radial distribution systems which are the IEEE 33- and IEEE 69-bussystems as a case study. Also, the paper optimizes the best allocation of the smart transformerto reduce the power losses of the grid

A New Distribution System Performance Approach to the Switch Allocation Problem Under Smart Grid Framework

This paper proposes a new vision of the Distribution System Switch Allocation problem considering new system performance measures. The mathematical model has been rebuilt with a new aggregated multi-objective formula, minimizing a newly developed performance index while achieving minimum annual energy lost. A new practical weighted combined system performance index, consisting of Reliability, Resiliency and Vulnerability, is applied and tested to be used by utilities replacing the common simple reliability index combination. The new model uses mixed integer design variables to determine the number, location and status of switches. A set of eight logical and technical constraints was applied to provide the best description of the real existing system constraints. A new algorithm of checking the system radial topology is also applied to the problem. The problem was solved using the Genetic Algorithm and was tested on a 54-bus real distribution test system, deemed more complicated than the test systems found in literature, to demonstrate its validity and effectiveness in real life systems

Smart Integration Based on Hybrid Particle Swarm Optimization Technique for Carbon Dioxide Emission Reduction in Eco-Ports

The increasing daily rate of environmental pollution, due to electrical power generation from fossil fuel sources in different societies, urges the researchers to study alternative solutions. These solutions can be summarized into either finding other clean, renewable sources or managing the available sources optimally. This research represents smart electrical interconnection management between some of the Egyptian seaports for optimal operation, with a clean sustainable environment as the target. The optimum ports’ commitment operation works through certain technical constraints to attain optimal economic and environmental factors. One of the main objectives of this study is the reduction of carbon dioxide (CO2) emission, which is released from the electrical power generation that covers the seaports demands. It is progressed through the green port smart commitment, by incorporating unpolluted and renewable energy resources. This study depends on the redesign of some Egyptian seaports to be green ports with eco-friendly electrical construction. According to the new electrical design, two out of the six studied seaports can be considered as renewable energy generation units consisting of Photovoltaic (PV) electrical generation resources. The new design of the seaports electrical network can be considered as a hybrid network, collecting both fossil fuel electrical power generation and PV sources. To gain benefits from the diversity in geographical behaviors, ports on the red sea and Mediterranean sea are integrated into the network cloud. Connecting ports on red and Mediterranean seas construct a network cloud, which supports the operation of the whole network under different conditions. Hybrid (weighted-discrete) Particle Swarm Optimization Technique (HPSOT) is an effective optimization technique which is applied to provide the optimum interconnection management between the eco-ports. It is developed based on some technical constraints which are the availability of the network buses interconnection, the voltage and frequency levels, and deviations due to the smart unit interconnection and the re-direction of the power flow. The HPSOT is targeted to minimize the economical cost and the harmful environmental impact of the seaport electrical network, while covering the overall network load. The HPSOT is programmed utilizing the Matlab program. It is tested on the six Egyptian seaports network that consists of El Dekheila, Alexandria, and Damietta on the Mediteranean and Port Said, Suez, and Sokhna port on the Suez canal and Red sea. It verifies its accurateness and efficiency in decreasing the combined cost function involving costs of CO2 emission. CO2 emission is reduced to 6% of its previous value for the same consumed electrical energy, that means it has a positive impact on retarding the greenhouse effect and climate change

Optimal energy management applying load elasticity integrating renewable resources

Abstract Urban growth aimed at developing smart cities confronts several obstacles, such as difficulties and costs in constructing stations and meeting consumer demands. These are possible to overcome by integrating Renewable Energy Resources (RESs) with the help of demand side management (DSM) for managing generation and loading profiles to minimize electricity bills while accounting for reduction in carbon emissions and the peak to average ratio (PAR) of the load. This study aims to achieve a multi-objective goal of optimizing energy management in smart cities which is accomplished by optimally allocating RESs combined with DSM for creating a flexible load profile under RESs and load uncertainty. A comprehensive study is applied to IEEE 69-bus with different scenarios using Sea-Horse Optimization (SHO) for optimal citing and sizing of the RESs while serving the objectives of minimizing total power losses and reducing PAR. SHO performance is evaluated and compared to other techniques such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Whale Optimization (WO), and Zebra Optimization (ZO) algorithms. The results show that combining elastic load shifting with optimal sizing and allocation using SHO achieves a global optimum solution for the highest power loss reduction while using a significantly smaller sized RESs than the counterpart