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.

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.

This paper is devoted to the incorporation of the steady state model of the unified powerflow controller (UPFC) in optimal power flow programs using injection model ofUPFC. This model is based on the representation of UPFC in steady state conditions bytwo voltage sources with certain reactances. The effects of UPFC control parameters ongeneration cost, system voltage profile, and system loadability are studied. Theproposed method has been applied to the IEEE 57-bus system with promising results.

The rapid depletion of fossil fuel resources and the growing evidence of global warming have made the utilization of renewable energy sources more attractive, especially with the increase in global energy demand. Most of the renewable energy sources are dependent on climate conditions. Therefore, Hybrid Renewable Energy Sources (HRES) can be highly efficient systems. In this paper, the Grey Wolf Algorithm (GWO) is proposed to find the optimal operation of PV/Wind HRES for a typical industrial load system with the objective of minimizing carbon emissions and life cycle cost (LCC). The algorithm is utilized to study two scenarios (PV/Wind and PV/Wind/Diesel generator) in comparison with conventional energy systems. The analysis of the results shows that both scenarios are more appealing than the conventional system. The first scenario can be considered the most attractive scenario for LCC and carbon emissions reduction. 60% and 90 % reductions are achieved, respectively. The second scenario can be considered the most attractive scenario for dump energy reduction. A 33% reduction is achieved compared to the first scenario.