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Reference papers represent cutting-edge research with high potential for significant impact in this field. Reference papers should be substantial original work that includes a method or approach, provides insight into future research directions, and describes possible research applications.
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Hafiz Abdul Muqeet 1, Haseeb Javed 2, Muhammad Naveed Akhter 3, Muhammad Shahzad 2, Hafiz Mudassir Munir 4, Muhammad Usama Nadeem 2, Syed Sabir Hussain Bukhari 4, Mikulas Huba 5*
Institute of Automotive Mechatronics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, 812 19 Bratislava, Slovakia
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Received: February 14, 2022 / Revised: March 9, 2022 / Accepted: March 15, 2022 / Published: March 18, 2022
Distributed generation connected to AC, DC or hybrid loads and energy storage systems is known as a microgrid. Campus microgrids are an important type of load. University campus microgrids typically include distributed generation resources, energy storage, and electric vehicles. The main purpose of microgrids is to provide sustainable and economical energy and reliable systems. Advanced Energy Management Systems (AEMS) ensure a smooth flow of energy to microgrids. In the last few years, we will analyze various aspects such as energy sustainability, demand response strategies, control systems and energy management systems with different types of optimization techniques used to optimize microgrid systems. This paper presents a comprehensive review of energy management systems in campus microgrids. The study also reviews existing literature reviews on various objective functions, renewable energy resources, and solution tools. In addition, research directions and related issues to be considered in future microgrid programming studies are also presented.
Distributed generation (DG) has the potential to overcome problems in global energy systems such as power stability, system reliability, grid overload, greenhouse gas emissions, and high consumption costs. Microgrid energy management systems in large commercial buildings present the challenge of minimizing grid load deviations and operating costs [1]. An energy management system (EMS) in a multi-energy microgrid (MG) can reduce operating costs and increase energy use efficiency [2]. However, distribution generation (DG) consists of renewable energy resources (RER) such as biomass, photovoltaics (PV), wind turbines (WT) and fuel cells (FC) and non-renewable energy sources such as be diesel generators (DiG). Composed of renewable energy sources. , gas engine (GE), microturbine (MT) [3].
There are different types of microgrids such as flexible load, DG and energy storage system (ESS). A typical microgrid model is described as a model that includes photovoltaics, diesel generators, grids and energy storage companies, as shown in Figure 1 [4]. It also includes a controller that efficiently manages the system by controlling the load to increase the solar power. This model is a two-way power flow as it receives tasks from houses, hostels and academic departments [5].
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In this model, users acting as consumers and prosumers are managed by an intelligent energy management system. It is generally understood that a microgrid that takes load from users efficiently is a better maintained, reliable and efficient microgrid system. Figure 1 also shows one of the popular microgrid models as an example.
DG is based on the control of distributed energy resources (DER) and optimal scheduling of microgrids. The optimal scheduling of energy production has a clear impact on the stability of the energy system [6]. Various power system programming techniques are used to improve microgrid power quality and voltage control based on real microgrid solutions to achieve green energy and create an efficient campus-to-campus smart microgrid with multiple scenarios of implementation aimed at obtaining sustainable energy to reduce GHG emissions [7].
Microgrids face many types of problems due to demand side fluctuations and voltage and frequency fluctuations. Energy management systems (EMS) usually face the problem of microgrids due to the lack of power generation sources. It aims to define the optimal use of DGs for powering electrical loads [8]. EMS works in centralized and distributed modes. The centralized mode is a mode in which energy exchange in the microgrid is primarily based on market prices. The decentralized mode is the opposite of the centralized mode because it is an autonomous energy exchange without market price limits [9]. Stability, efficiency and energy conservation are also key issues for microgrids. This is due to power reversal in generating units, voltage fluctuations, transient modes in the microgrid, sudden frequency fluctuations in isolated modes of operation, and uncertainty in microgrid supply and demand. An angular tilt is required for correct load distribution, especially in weak system conditions. EMS also presents several challenges. To overcome these challenges, a detailed overview of several microgrids was developed to discuss the key issues of energy management systems [10].
An overview of some microgrids with installed components is shown in Table 1. It brings together analyzes of different microgrids to provide a comprehensive review, considering load types, optimization techniques, and results.
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This study also aims to critically analyze a number of microgrids to provide an overview of multiple campus microgrids, analyze campus energy management systems, and provide solutions for campus optimization. and Focuses on the field of campus microgrids, with an emphasis on industrial microgrids and prosumer microgrids. Today, many power producers aim to produce electricity sources, often referred to as “prosumers”. The contribution of this new research is to help other researchers in the field of energy management in campus microgrids. This is because it presents a systematic overview of various literatures, considering installed systems and multiple solution-focused approaches. Here. This novelty also helps us explore new dimensions in distributed generation. The innovative approach of this work is also useful for researchers aiming to provide novelties in the fields of campus microgrids, demand management, and optimal scheduling of distributed microgrids.
This research paper was further organized as described in Section 2. Energy Management in Campus Microgrid with Distributed Generation. Section 3 discusses optimal microgrid scheduling. Section 4 describes simulation tools for optimal microgrid scheduling. Finally, the research questions and conclusions are presented in Sections 5 and 6.
Microgrids mainly consist of energy storage systems (ESS), distributed generation (DG) resources and loads. Distributed generation includes different types of power generation technologies, such as combined cycle systems and solar panels [31]. To analyze the energy management of microgrids, we can discuss the self-healing power of microgrids to make them self-sustaining [32]. In a centralized system, self-reliance offers communities an effective way to deal with independent energy providers that use fossil fuels. This gives remote community members an easy way to connect to utilities and access better energy. The independence helps the microgrids to act as autonomous energy producers [33].
On the other hand, a combined system consisting of WT, DiG, FC and PV is developed in Fig. 2 to demonstrate the self-resilience of microgrids and how to manage AC or DC loads in a community.
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In Figure 2, the hybrid AC/DC microgrid units are interconnected and the demand load is balanced using the EMS. In MG1, battery, wind power and load are connected via AC-BUS. Similarly, MG2 components are connected to AC BUS (1–2) and CL (1–2) is the converter connected to the system. This model represents an interconnected microgrid system designed to independently manage community tasks.
Here we describe a microgrid system with different EMS systems, optimization techniques and multiple solutions presented for different renewable energy resources. Several authors have reviewed these distributed generations of the various microgrid systems briefly described here.
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