Journal of Green Energy Research and Innovation
Volume:2 Issue: 1, Winter 2025

This paper delves into the meticulous optimization of distributed energy resources and their storage within a conventional microgrid framework. The optimization endeavor leverages an array of cutting-edge technologies including photovoltaic, wind, fuel cells, micro-turbines, and batteries, with the dual objectives of curtailing operational expenses and fortifying system reliability. To attain these objectives, the article employs a refined algorithm derived from the Grey Wolf Optimization technique. Furthermore, simulations are executed under two distinct scenarios. In the first scenario, the presumption is that all distributed energy resources within the microgrid are exploitable, whereas in the second scenario, spatial constraints necessitate the exclusion of photovoltaic and wind turbine resources. Simulation outcomes evince that post-implementation of energy management via metaheuristic algorithms, there is a discernible reduction in the operational costs of the microgrid alongside an enhancement in system reliability. Additionally, the elimination of photovoltaic and wind resources results in escalated costs and grid blackout within the microgrid. In summary, the simulation findings affirm the superior efficacy of the proposed modified Grey Wolf algorithm in addressing energy management quandaries in comparison to the Particle Swarm Optimization algorithm.

As oil resources become increasingly scarce, the need to transition to renewable energy sources is ‎critical for sustainable living. The United Nations first emphasized the importance of sustainable ‎development in 1987, highlighting the "central role" of energy in this effort. After years of research, ‎innovative solutions for sustainable energy have emerged, with energy production from wastewater ‎showing significant potential. Wastewater can be converted into biogas through anaerobic digestion, ‎where microorganisms break down organic matter in the absence of oxygen to produce methane, which ‎can be used for electricity, heat, or fuel. This process not only aids in reducing greenhouse gas ‎emissions but also supports environmental protection and waste management. The growing demand for ‎renewable energy has sparked significant interest in these techniques, which utilize bacteria to generate ‎electricity, further demonstrating the potential of wastewater as a sustainable energy source. Using ‎wastewater for energy not only lowers operational costs but also allows treatment plants to generate ‎energy on-site, reducing reliance on external energy sources and lowering carbon emissions. Exploring ‎these renewable energy sources is crucial, particularly given the large volumes of wastewater generated ‎from agricultural, industrial, and domestic activities. This paper reviews the potential of wastewater as a ‎green energy source, discussing specific technologies for treating various wastewater types and the ‎associated challenges and opportunities. By examining successful case studies and emerging trends, it ‎aims to advance green energy solutions that promote both environmental sustainability and economic ‎growth‎‎.

In this article, in order to maintain the voltage and frequency of a multi-bus island microgrid, a robust control strategy ‎is proposed. The microgrid under study is a medium voltage distribution system consisting of several inverter-based ‎distributed generation (DG) units, and a combination of balanced and unbalanced local loads. Based on the Grid- ‎forming/Grid-following structure, a robust voltage controller is designed based on the adaptive backstepping control ‎method to keep the voltage and frequency of the Grid-forming unit at predefined values.  In addition, to adjust the ‎active/reactive powers of the Grid-following units, a direct power controller based on adaptive input-output feedback ‎linearization control method is proposed.  The negative sequence component of unbalanced local loads current is ‎compensated by the proposed power controller. Only local measurements are used in designing of the controllers; ‎therefore, the proposed method is independent of the topology of microgrid, the parameters of the system, and the ‎dynamics of loads. The controllers presented in this article are robust and stable against various disturbances and ‎parameter uncertainties. after outage of Grid-following unit, the power generated by the Grid-forming unit is adaptively ‎adjusted so that the absence of generation at PC2 is offset. In addition, despite the mismatched filter resistance and ‎accidental outage of Grid-following unit, even under non-local unbalanced load condition the proposed voltage ‎controller robustly regulates the voltage waveform of MG with a reasonable transient. Validity and effectiveness of the ‎proposed control strategy are shown based on time domain studies in MATLAB/Simulink environment‎.

In recent years, distributed production as a source of local loads and continuous economic exploitation has gathered ‎attention. On this thread, this study focuses on distributed production using a photovoltaic package with batteries so ‎that the power drawn from the distributed generation system for injection into the global network or receiving it is ‎adjusted based on the battery charge status. The goal is to absorb the maximum power received from the photo-voltaic ‎system at any temperature and hypothetical radiation. If the battery charge is not optimal, part of this power is applied ‎to the battery for charging. By presenting a suitable structure, a photovoltaic system with a battery package is presented ‎as a distributed generation source with the design of appropriate controllers. The results showed that at any temperature ‎and radiation, the maximum power received from the photovoltaic system could be estimated. By controlling switching, ‎a converter, the required amount of energy can be obtained from the photovoltaic system. It can be concluded that such ‎a structure, as a desirable distributed generation source, is realized. With the proper design of the necessary controllers, ‎optimal management can be done for power management.‎

In this paper, a new peer-to-peer (P2P) pricing mechanism based on Flexi User and Pool Hub schemes is proposed in a community of buyers using battery storage systems to ensure that all customers in a community enjoy economic benefits. The proposed mechanism does not only consider the power surplus and shortage relationship, but also considers the power grid Real-Time Price (RTP) and Feed-in Tariff (FiT), which reflects the power system demand, where the price is high during peak demand and lower during off-peak. Demand is then implemented by a demand response (DR) program to encourage consumers to manage energy consumption, reduce stress on the power grid, and ensure that energy exchange between peers does not violate grid constraints. Results show that in addition to demand response in the grid, in the Flexi User scenario, the total savings to society from the combination of storage and P2P collaboration lead to a 24.25% reduction in electricity bills compared to a reference case (neither storage nor P2P trading). While the monetary savings in the Pool Hub market is up to 25.53%, this requires more direct P2P trading of distributed energy resources.

Today, the attention to energy security, the increase in the need for electrical energy and the need to create new power plants, especially the power plants that use renewable energy, has increased significantly both in Asia and globally. Wind power is expected to make the largest contribution to global decarburization, ranking first or second in terms of projected capacity by 2050. This type of power plant directly uses natural energy as fuel. And as a result, climate change affects the efficiency of these power plants. Two parameters of the natural phenomenon of wind, which include wind speed and direction, are the main factors of wind power plant efficiency. The science of remote sensing is the process of identifying and monitoring the physical characteristics of a remote area by means of satellites. Google Earth Engine is an artificial intelligence to use this knowledge. Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Google Earth Engine provides us with these  parameters. In this article, by collecting and then analyzing these data, we try to choose suitable candidate locationsfor the establishment of these two types of power plants, and based on priority, we provide a list for the establishment of solar and wind power plants