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

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

A probabilistic approach for maximizing the islanding success in microgrids

With Increasing electricity demand, microgrids nowadays play an important role in smart grids which demand a systematic approach for its optimal design and performance enhancement. Microgrids development is propitious for the electric energy industry from several aspects including environmental aspects, improvement of system operation and power quality, cost saving and other market related issues. In the IEEE Standard 1547.4, splitting large distribution networks into a number of smaller networks can provide enhanced operation and control. In this paper, a distribution test system is considered for optimum design of microgrids. The design considers maximizing the success indicant of the constructed microgrids. The islanded operation of microgrids requires serving important loads when the main supply fails. Thus, the success of the islanding process can be a challenging issue for planners to enhance the distribution system reliability. The indicant formulated in this paper incorporates the crucial conditions for successful operation of microgrids such as power imbalance and voltage constraints. A radial distribution system of 69 buses is chosen for this research. The selected system has a typical fuel mix of distributed generators such as biomass generators, photovoltaic units and wind turbines. The design problem is solved using the recently developed back tracking search optimization algorithm. The intermittent nature of distributed generators is taken into consideration through a probabilistic approach

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

On the Natural Frequency of Oscillations of Induction Motors

For transient stability analysis, the rotor dynamics of the induction motor have to be included. These dynamics affect the system stability when severe disturbances hit it and cause frequency deviations. For large systems, frequency deviations are small. However, it may cause loss of synchronism and break the system into smaller areas. Motor loads are sensitive to system frequency deviations. Any change in the grid frequency, changes extremely the slip. This follows by changes of the motor torque and the motor speed. The demanded active and reactive powers change as well. Natural frequencies of induction motors is considered a unique property has a great effect on its behavior during different operation conditions. This work presents the performance of the induction motors through different power systems. Based on time domain simulation models study the natural frequency of induction motors, their response in normal and abnormal operation is analyzed to illustrate the dynamics associated

A Reconfigured Whale Optimization Technique (RWOT) for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt

This paper studies the effect on the rate of growth of carbon dioxide emission in seaports’ atmosphere of replacing a part of the fossil fuel electrical power generation by clean renewable electrical energies, through two different scheduling strategies. The increased rate of harmful greenhouse gas emissions due to conventional electrical power generation severely affects the whole global atmosphere. Carbon dioxide and other greenhouse gases emissions are responsible for a significant share of global warming. Developing countries participate in this environmental distortion to a great percentage. Two different suggested strategies for renewable electrical energy scheduling are discussed in this paper, to attain a sustainable green port by the utilization of two mutual sequential clean renewable energies, which are biomass and photovoltaic (PV) energy. The first strategy, which is called the eco-availability mode, is a simple method. It is based on operating the renewable electrical energy sources during the available time of operation, taking into consideration the simple and basic technical issues only, without considering the sophisticated technical and economical models. The available operation time is determined by the environmental condition. This strategy is addressed to result on the maximum available Biomass and PV energy generation based on the least environmental and technical conditions (panel efficiency, minimum average daily sunshine hours per month, minimum average solar insolation per month). The second strategy, which is called the Intelligent Scheduling (IS) mode, relies on an intelligent Reconfigured Whale Optimization Technique (RWOT) based-model. In this strategy, some additional technical and economical issues are considered. The studied renewable electrical energy generation system is considered in two scenarios, which are with and without storage units. The objective (cost) function of the scheduling optimization problem, for both scenarios, are developed. Also, the boundary conditions and problem constraints are concluded. The RWOT algorithm is an updated Whale Optimization Algorithm (WOA). It is developed to accelerate the rate of reaching the optimal solution for the IS problem. The two strategies simulation and implementation are illustrated and applied to the seaport of Damietta, which is an Egyptian port, located 10 km to the west of the Nile River (Damietta Branch). The scheduling of PV and biomass energy generation during the different year months is examined for both strategies. The impact of renewable electrical energies generation scheduling on carbon dioxide emission and consequently global warming is discussed. The saving in carbon dioxide emission is calculated and the efficient results of the suggested models are clarified. The carbon dioxide emission is reduced to around its fifth value, during renewable energy operation. This work focuses on decreasing the rate of growth of carbon dioxide emission coming from fossil fuel electrical power generation in Egypt, targeting, sustainable green seaports, through three main contributions in clean renewable electrical energies scheduling,. The contributions are; 1-presenting the eco-availability mode for minimum gifted biomass and PV energy generation, 2-developing and progressing the IRWOT scheduling strategy for both scenarios (with and without storage unit), 3-defining the scheduling optimization problem boundary conditions and constraints

Smart Hybrid Micro-Grid Integration for Optimal Power Sharing-Based Water Cycle Optimization Technique

Micro-Grid (MG) with hybrid power resources can supply electric loads independently. In case of surplus power, the neighborhood micro-grids can be integrated together in order to supply the overloaded micro-grid. The challenge is to select the most suitable, optimal and preferable micro-grid within a distributed network, which consists of islanded MGs, to form that integration. This paper presents an intelligent decision-making criteria based on the Weighted Arithmetic Mean (WAM) of different technical indices, for optimal selection of micro-grids integration in case of overloaded event due to either unusual increase in consumed power or any deficiency in power generation. In addition, overloading is expected due to excess increase or decrease in weather temperature. This may lead to extreme increase of load due to increase of air conditioning or heating loads respectively. The proposed arithmetic mean determination based on six multi-objective indices, which are voltage deviation, frequency deviation, reliability, power loss in transmission lines, electricity price and CO2 emission is applied. This work is developed through three main scenarios. The first scenario studies the effect of each index on the integrated micro-grid formation. The second scenario is the biased optimization analysis. In this stage, the optimal micro-grids integration is based on intentionally chosen multi-objective index weights to fulfil certain requirements. The third scenario targets the optimal selection of the multi-objective indices’ effectiveness weights for power system optimum redistribution. The sharing weights of each index will be optimally selected by Water Cycle Optimization Technique (WCOT) and Genetic Algorithm (GA) addressing the system optimal power sharing through optimum micro-grids re-formation (integration). WCOT and GA are simulated using MATLAB (R2017a, The MathWorks Ltd, Natick, MA, USA). The developed work is applied to a distributed network which consists of a five micro-grid tested system, with one overloaded micro-grid. The three modules are utilized for multi-objective analysis of different alternative micro-grids. Both WCOT and GA results are compared. In addition, it is investigated to find and validate the optimum solution. Final decision-making for optimal combination is determined, aiming to reach a perfect technical, economic and environmental solution. The results indicate that the optimal decision may be modified after each individual index weight exceeds a specific limit

Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs) with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired) power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs) which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED). To reduce combined emission costs, integrating more renewable energy resources (RER) and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired) power plants. The Water Cycle Optimization Algorithm (WCOA) is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO2 emission costs applying the Water Cycle Optimization Algorithm (WCOA). To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP) algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA) as a meta-heuristic technique

FPGA Eco Unit Commitment Based Gravitational Search Algorithm Integrating Plug-in Electric Vehicles

Smart grid architecture is one of the difficult constructions in electrical power systems. The main feature is divided into three layers; the first layer is the power system level and operation, the second layer is the sensor and the communication devices, which collect the data, and the third layer is the microprocessor or the machine, which controls the whole operation. This hierarchy is working from the third layer towards first layer and vice versa. This paper introduces an eco unit commitment study, that scheduling both conventional power plants (three IEEE) thermal plants) as a dispatchable distributed generators, with renewable energy resources (wind, solar) as a stochastic distributed generating units; and plug-in electric vehicles (PEVs), which can be contributed either loads or generators relied on the charging timetable in a trustworthy unit commitment. The target of unit commitment study is to minimize the combined eco costs by integrating more augmented clean and renewable energy resource with the help of field programming gate array (FPGA) layer installation. A meta-heuristic algorithm, such as the Gravitational Search Algorithm (GSA), proves its accuracy and efficiency in reducing the incorporated cost function implicating costs of CO2 emission by optimally integrating and scheduling stochastic resources and charging and discharging processes of PEVs with conventional resources power plants. The results obtained from GSA are compared with a conventional numerical technique, such as the Dynamic Programming (DP) algorithm. The feasibility to implement GSA on an appropriate hardware platform, such as FPGA, is also discussed