Optimal sizing and siting of smart microgrid components under high renewables penetration considering demand response

The purpose of this article is to determine the size and place of different components in microgrids (MGs) including renewable energy resources (RERs). Various factors like reliability, the uncertainty of wind speed, solar irradiance, load, and load growth are considered. The Ekbatan residential complex is studied as the pilot case study placed in Tehran, Iran. Ekbatan complex has three separate sets of buildings called phase 1, 2, and 3 considered as smart MGs. The multi‐objective optimisation problem is solved considering RERs uncertainties, improving reliability and power quality and minimizing power loss by particle swarm optimisation algorithm. Different constraints in terms of voltage, frequency, resources, and energy storage systems (ESSs) capacity are taken into consideration. The effect of load growth, photovoltaic (PV) and ESSs placement, changing the capital cost of RERs, and demand response of controllable loads are studied on optimal sizing and siting. The proposed method is tested on a wind turbine/PV/fuel cell (FC)/hydrogen tank MGs system and the optimal sizing and siting of mentioned sources could decelerate the rate of increase in the total cost of MG considering the load growth.©2019 IET. This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

Sustainable rural electrification through solar PV DC microgrids—An architecture-based assessment

Solar photovoltaic (PV) direct current (DC) microgrids have gained significant popularity during the last decade for low cost and sustainable rural electrification. Various system architectures have been practically deployed, however, their assessment concerning system sizing, losses, and operational efficiency is not readily available in the literature. Therefore, in this research work, a mathematical framework for the comparative analysis of various architectures of solar photovoltaic-based DC microgrids for rural applications is presented. The compared architectures mainly include (a) central generation and central storage architecture, (b) central generation and distributed storage architecture, (c) distributed generation and central storage architecture, and (d) distributed generation and distributed storage architecture. Each architecture is evaluated for losses, including distribution losses and power electronic conversion losses, for typical power delivery from source end to the load end in the custom village settings. Newton–Raphson method modified for DC power flow was used for distribution loss analysis, while power electronic converter loss modeling along with the Matlab curve-fitting tool was used for the evaluation of power electronic losses. Based upon the loss analysis, a framework for DC microgrid components (PV and battery) sizing was presented and also applied to the various architectures under consideration. The case study results show that distributed generation and distributed storage architecture with typical usage diversity of 40% is the most feasible architecture from both system sizing and operational cost perspectives and is 13% more efficient from central generation and central storage architecture for a typical village of 40 houses. The presented framework and the analysis results will be useful in selecting an optimal DC microgrid architecture for future rural electrification implementations

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Taking the Power Back: Designing a Replicable Neighborhood Grid

Transitioning to alternative energy can help the environment and lower energy bills, but the process can be complicated, and the up-front costs can be high for individual homeowners. In collaboration with the Australian Energy Foundation, our team worked with residents of Halpin Street in Brunswick West, VIC, investigating the path they might take and the options they might consider in setting up a neighborhood microgrid. Through the literature review, interviews with local companies and residents, and solar mapping software, we identified the best options for energy generation, storage, monitoring systems, and AC/DC inverters, sharing our recommendations with residents and outlining our process so other neighborhoods might work together to pursue this path

EFFECTS OF CONNECTING A SCATTERED SOLAR GENERATION UNIT TO THE GRID ON THE CLOUD PASSAGE USING OPTIMIZATION ALGORITHMS

Today, limitation of fossil fuel resources and other issues such as the possibility of the depletion of fossil energy reserves, global warming, environmental pollution, price instability, and the growing need for industrial and urban centers for energy have prompted the international community to seek appropriate alternatives. Such examples are nuclear energy, solar energy, geothermal energy, wind energy, and ocean waves. Renewable energy is generated owing to the simplicity of the applied technology compared to nuclear energy technologies. On the other hand, such energies play a key role in new energy systems in the world similar to nuclear waste. The increasing use of renewable energies has given rise to significant complications. One of the main operational issues in this regard is the uncertainty of electricity generation by solar power plants, which is caused by the passage of clouds. The present study aimed to investigate the effects of cloud passage on the production of solar power plants. Initially, a control system was designed to control a high-penetration solar power plant in the network, and the maximum allowable percentage of penetration was calculated for different loads. For this purpose, three algorithms (DE, PSO, and ICA) were used to determine the MPPT of the solar arrays in shady conditions, as well as the MPPT point of the solar arrays. According to the results, the colonial competition algorithm was faster compared to the other algorithms

Microgrids/Nanogrids Implementation, Planning, and Operation

Today’s power system is facing the challenges of increasing global demand for electricity, high-reliability requirements, the need for clean energy and environmental protection, and planning restrictions. To move towards a green and smart electric power system, centralized generation facilities are being transformed into smaller and more distributed ones. As a result, the microgrid concept is emerging, where a microgrid can operate as a single controllable system and can be viewed as a group of distributed energy loads and resources, which can include many renewable energy sources and energy storage systems. The energy management of a large number of distributed energy resources is required for the reliable operation of the microgrid. Microgrids and nanogrids can allow for better integration of distributed energy storage capacity and renewable energy sources into the power grid, therefore increasing its efficiency and resilience to natural and technical disruptive events. Microgrid networking with optimal energy management will lead to a sort of smart grid with numerous benefits such as reduced cost and enhanced reliability and resiliency. They include small-scale renewable energy harvesters and fixed energy storage units typically installed in commercial and residential buildings. In this challenging context, the objective of this book is to address and disseminate state-of-the-art research and development results on the implementation, planning, and operation of microgrids/nanogrids, where energy management is one of the core issues

Investigations on performance enhancement measures of the bidirectional converter in PV–wind interconnected microgrid system

This is the final version. Available from MDPI via the DOI in this record. In this work, a hybrid microgrid framework was created with the assistance of a photovoltaic (PV) and wind turbine (WT) generator. Additionally, bidirectional control mechanisms were implemented where an AC system was integrated with permanent magnet synchronous generator (PMSG)-based WT and a DC system was integrated with a sliding mode algorithm controlled maximum power point tracker (MPPT)-integrated PV system. The wind and PV interconnected microgrid system was mathematically modeled for steady-state conditions. This hybrid microgrid model was simulated using the MATLAB/SIMULINK platform. Optimal load management strategy was performed on a chosen hybrid microgrid system. Various case studies pertaining to connection and disconnection of sources and loads were performed on the test system. The outcomes establish that the system can be kept up in a steady-state condition under the recommended control plans when the network is changed, starting with one working condition then onto the next