Introduction of Campus Microgrids: Comparison
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Campus Microgrids are a scattered group of power sources and electrical loads that are usually synchronous with the primary grid, called the utility grid. The multiple uncertainties in a microgrid, such as limited photovoltaic generations, ups and downs in the market price, and controlling different loads, are challenging points in managing campus energy with multiple microgrid systems and are a hot topic of research in the current era. Microgrids deployed at multiple campuses can be successfully operated with an exemplary energy management system (EMS) to address these challenges, offering several solutions to minimize the greenhouse gas (GHG) emissions, maintenance costs, and peak load demands of the microgrid infrastructure. 

  • campus microgrids
  • energy management system

1. Introduction

Over the years, the surge in demand for electricity has directly led to reducing reserves of fossil fuels such as petroleum, natural gas, and coal. This affects the environment through the direct increase in greenhouse gas (GHG) emissions. Power systems worldwide integrate renewable energy resources such as solar–PV, tidal energy, biomass energy, and wind energy to alleviate the problems mentioned earlier [1]. Microgrids provide an opportunity to offer a solution to reduce greenhouse gas emissions while providing reliable power to fulfill the load demand. Campus Microgrids are a scattered group of power sources and electrical loads that are usually synchronous with the primary grid, called the utility grid [2]. Residential electrical energy sources strongly assist microgrids, and the distribution of energy from the primary grid is performed at long distances and is difficult to achieve. Microgrids that are autonomous and self-reliant are called stand-alone, autonomous, or isolated microgrids [3]. A microgrid can operate in isolated mode and grid-connected mode and handles the transitions between both modes. In isolated mode, both the real and reactive power are produced inside the microgrid, and the electrical energy storage systems (ESSs) can stabilize the local load demand. In the utility grid or grid-connected mode, ancillary services are offered in such a scenario, considering the trading activity between the utility and the microgrid [4]. Microgrids provide a proposition with respect to extreme events and natural hazards such as earthquakes, floods, hurricanes, tornadoes, storms, etc., with the advantages that they utilize the latest technologies and techniques to overcome the system’s daily challenges, such as the need for power in an emergency [5].

Institutional campuses or universities typically fulfill the main requirements to convert their energy supply into campus microgrids. Their operations are monitored from a central controller, as shown in Figure 1, to manage the loads and generation units for every campus building [6]. In Figure 1, multiple sources are connected to the electrical grid in which the power electronics interface receives the power from the microgrid power distributor and converts the power to the voltage and frequency required. The main role of the current survey paper was to analyze different types of campus microgrids with multiple resources that are installed on various campuses, including conventional energy resources, renewable energy sources, demand-side management (DSM), and energy storage systems (ESSs). Campus microgrids are reviewed based on the optimization techniques, objective functions (OFs), and modeling techniques. Different types of solutions are provided in the current survey paper for multiple campus microgrids.

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Figure 1. Schematic diagram of a microgrid.

Numerous campus microgrids have been installed globally to serve as a testbed and reveal the economic benefits and profits of utilizing such a system in the utility grid.

In this paper, some campuses are discussed as actual microgrids, and some are discussed as simulated microgrids. Those for which the authors mentioned developing the model of a microgrid or proposed a microgrid are the simulated microgrids. Various campus microgrids installed at multiple locations are discussed here.

Nemanja et al. [7] presented a microgrid model for the University of Novi Sad, Serbia. This overall microgrid model consists of two solar–PV, two wind-generating microturbines, biogas-based turbines, a BESS, an EV system which are acting as a prosumer, a microcontroller that connects it to the primary grid, and consumers. They also analyzed the feasibility and economics in which the installation cost, energy generation cost, and GHG emissions were reduced, although not considering a risk assessment for the campus microgrid. Figure 1 shows the general model of a microgrid that contains PV modules, wind generators, controllers, and an electrical grid; an overview of campus microgrids at different locations is shown in Figure 2.

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Figure 2. Overview of campus microgrids at different locations.

Similarly, Stefano et al. [8] presented research on smart microgrids and the energy management of multiple campuses, efficient and innovative designs, and operating and nonoperating grids in grid-connected and islanded modes. This study was presented at the University of Genova, Savona Campus, Italy. It aimed to improve the grid system, maintain efficiency, and overcome energy efficiency and sustainability issues, but did not address issues such as frequency regulation for the campus microgrid. However, a feasibility and techno-economic analysis was also developed in [9] by Kritiawan for a PV integrated power plant at Sebelas Maret University, Indonesia, to mitigate the issues of energy management in the Sebelas campus microgrid; however, the power quality [10] and voltage regulation [11] were not addressed, although were also needed for the project. On the other hand, the Pulau Ubin daily operated microgrid based in Singapore was examined by Valentina in [12]. This testbed consisted of an ESS, a solar–PV system, and three biodiesel generators. A system was developed to improve the power factor and voltage deviation. The main objective was to reduce the operational cost of the MG and the voltage variation of the network, but it did not focus on power improvements, the P2P trading mechanism, or frequency regulation for the campus.

Some of the related studies, especially the energy management structure of the microgrid, have considered optimal scheduling, an ESS, and PV. Many researchers here have also investigated the integration of an ESS in a microgrid while also checking the feasibility of solar–PV; however, some other researchers merely focused on the cost savings of PVs and optimum scheduling of the ESS.

Distributed generation (DG), on the other hand, is known as on-site generation and can also be called decentralized generation [13]. It can be defined as on-site electricity generation facilities to transmit the power over large distances from grid-like coal power plants. It can be utilized to minimize the effects of GHG emissions and to improve system efficiency and reliability. However, DG consists of photovoltaics (PVs) [14], wind turbines (WTs) [15], biomass [16], and fuel cells (FCs) [17] as renewable units, whereas other sources such as diesel generators (DiGs) [18], microturbines (MTs) [19], and tidal and geothermal gas engines (GEs) [20] are the sources of conventional units [21]. The microgrid component model is depicted briefly in Figure 3. This generic model consists of flexible and nonflexible energy sources. The flexible energy sources comprise three types: DR-programs, energy storage, and conventional energy resources [22]. The flexible energy sources contain storage systems that comprise fuel cells, batteries, flywheels, supercapacitors, and batteries. Flexible energy sources also contain conventional energy resources such as microturbines, diesel generators, and combustion turbines. Moreover, they also contain the DR programs, further categorized into price-based programs and incentive-based programs, as shown in Figure 3.

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Figure 3. Microgrid components.

The non-flexible energy sources include renewable energy resources such as PV, wind, biomass, and tidal energy sources, as shown in Figure 3. The analysis has been evaluated based on the literature of some campus microgrid review papers which describes which type of campus load is connected with components such as those mentioned in Table 1.

Table 1. A review of the literature of campus microgrids with multiple sites.

Refs. Campus Optimization MethodsTechnical Aspects
Advantages Components Load Type
Disadvantages Objectives and Applications PV BESS Wind Biomass DG 1 MT 2 EV 3 SC 4 FC 5 CHP 6 Campus/Building
[4] University of Cyprus (UCY) ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus
[6
Deterministic Methods MILP [74] Mixed-integer linear programming (LP) resolves the complications quickly and comprehensively. Their linear constraint lies in the feasible convex region, aiming to find the optimum global point and an exact solution. Economic and stochastic analysis. It contains limited capability for applications which do not have continuous and differentiable objective functions. MILP is commonly used for optimization problems. It is easy to use with CPLEX Solver, which is good software available. It is used for unmanned aerial vehicle (UAVs) in planning their flight paths.
] University of Malta ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus
Dynamic Programming (DP) [75] Splitting the problems into their sub-sequent parts and then optimizing them to find the optimal solution. It contains a large number of recursive functions; therefore, it is time-consuming. It is also used as an optimization problem. It solves problems such as reliability design problems, robotics control, and flight control. [7] University of Novi Sad, Serbia ✓ ✓ ✓ ✗ ✓ ✗ ✓ ✗ ✗ ✗ Campus


Reliability
MINLP [76] Solves the problems with simple operations and contains many optimal solutions that take positive benefits over MILP. It is time-consuming. Mixed-integer nonlinear programming (MINLP) deals with an optimization problem involving discrete and continuous variables, as well as nonlinear variables in the objective function. [23] Chalmers University of Technology, Sweden ✓ ✓ ✗
Metaheuristic

Methods		 Particle Swarm Optimization (PSO) [77]✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus

Building
Greater efficiency while resolving the optimization problems. Easy adaptation for various kinds of optimization problems and reporting near-optimal solutions in a reasonable time. Complex computation while solving an optimization problem.

The search process may face entrapment in local optima/minima regions. PSO can be used for many optimization problems, such as energy-storage optimization. It can also be used for visual effects in videos. [24] American University of Beirut (AUB), Lebanon ✓ ✓ ✗ ✗ ✓ ✗ ✗ ✗ ✗ ✗ Campus
[25]

Sustainability
Genetic algorithms (GA) [78] Based on population-type evolutionary algorithms that comprise mutation, selection, and crossover to search for an optimal solution for a particular problem. They also have a suitable convergence speed and can adapt easily for various kinds of optimization problems with reporting near-optimal solutions in a reasonable time. The parameters must be met for the operations of mutation, selection, and crossover while solving.

It also has no guarantee of attaining the best solution. The search process may face entrapment in local optima/minima regions, similarly to PSO.		 Genetic algorithms have several applications in natural sciences such as in computer architecture to find an extensive solution. It is used to learn the robot’s behavior and is also used in image processing. It is also used for file allocation in distributed systems. Tezpur University, India ✓ ✓ ✗ ✗ ✓ ✗ ✗ ✗ ✗ ✗ Campus
Artificial Fish Swarm [79] High accuracy, contains few parameters, has flexibility, and fast convergence. It also adapts easily for various kinds of optimization problems with reporting near-optimal solutions in a reasonable time. It has the same advantages as genetic algorithms, but it has disadvantages without mutation and crossover. Attaining the best solution is also no guarantee. Moreover, the search process may also face entrapment in local optima/minima regions, similarly to GA. Artificial fish swarm is used for fault tolerance, fast convergence speed, good flexibility, and high accuracy. It commonly uses the general method to solve all types of problems such as prey, follows, and swarms. Other applications of AFS are neural network learning, global optimization, color quantization, and data clustering. [26] Valahia University of Targoviste, Romania ✓ ✓ ✓ ✗ ✗ ✗ ✗ ✗
✓
✓
✗
✗
Campus
[
44
]
Aligarh Muslim University, India ✓ ✓ ✗ ✗ ✓ ✗ ✗ ✗ ✗ ✗ Campus
[45] North China Electric-Power University, Beijing, China ✓ ✓ ✓ ✗ ✗ ✗ ✓ ✗ ✗ ✓ Campus

1 DG denotes distributed generation. 2 MT denotes the microturbine. 3 EV denotes the electric vehicle. 4 SC denotes super capacitor. 5 FC denotes fuel cells. 6 CHP denotes combined heat and power.

The main contributions of this survey paper are:

51][52] include the artificial bee colony (ABC), particle swarm optimization (PSO), simulated annealing (SA), genetic programming (GP), differential evolution (DE), genetic algorithm (GA) and many multi-objective problems that involve contradictory spatial objectives in the process of decision making. The constraints and objective functions used in linear programming are special linear functions having a whole and real-valued decision variable. Dynamic programming is also termed the DP programming method used for many complex problems sequenced and discretized. To deal with such issues, they can be categorized as sub-problems that can be solved optimally. These results are then covered to create an appropriate solution to solve the main problem [53] optimally.

The metaheuristics approach is another effective alternate in the optimization of microgrids. Heuristic methods are combined to find an adequate solution using genetic algorithms, statistical mechanisms, and biological evolution to achieve the optimal control and operation of microgrid power [54]. Predictive control methods are used to forecast electricity generation and effectively manage the energy stored already. This method classically associates both control and stochastic programming. The most notable among these methods are predicting the weakening of elements in the grid, especially energy storage systems [55]. Optimization techniques based on multi-agent approaches allow the proper decentralized management of campus microgrids. These multi-agents include various loads, storage systems, and distributed generators linked with one another to achieve minimal cost of the microgrids [49].

A detailed literature review on multiple campus microgrids with various techniques, components, and results are summarized in Table 2. This represents a detailed analysis of campus microgrid topics with the addition of their illustrated results, techniques, and components. Presenting this type of analysis benefits various authors who contribute to the field of smart grids. Table 2 is helpful for those researchers who search for optimization techniques or algorithms in different literature reviews. The detailed analysis is presented in Table 2.

Table 2. A survey of optimization techniques used in campus microgrids.

Ref. Location Components Optimization Techniques for Energy Management Economic Analysis
[56] Oregon State University, Corvallis, Oregon, USA Smart meters

2 Solar–PV arrays		 Linear optimization Energy management and voltage-regulated
[
[
60
]
McNeese State University, Lake Charles, Louisiana, USA 15 kW PV system
Artificial Intelligence Methods Artificial Neural Network [80]

2/65 kW CHP generators		 Fast Fourier transform (FFT) algorithm Controlling water flow resulted in higher thermal recovery
Its evaluation time is faster than previous algorithms; it deals with problems to obtain the target function values for real-valued, discrete values, etc. It is hardware-dependent and requires parallel processors. It gives untold solutions, does not give a clue for the solution how it has been done. Artificial neural networks are used in handwriting recognition, image compression, and stock exchange forecasting. [61] AMU (Ali Garh Muslim University), India PV

Grid

wind		 HOMER analysis NPC (Net Present Cost): USD 17.3 million/year

CO2 emissions: 35,792 kg/year.
Fuzzy Logic [81] The structure of fuzzy logic is easy to understand, which highly encourages developers to use it for controlling machines. Maintaining the accuracy with fuzzy logic is quite difficult sometimes.
  • Campus microgrids are studied to depict the different types of sources installed at various campuses, including conventional energy resources, renewable energy sources, demand-side management (DSM), and energy storage systems (ESSs);
  • Campus microgrids are reviewed based on optimization techniques, objective functions (OFs), and modeling techniques;
  • Campus microgrids are studied as innovative campus microgrid scenarios that serve as smart decision approaches for university campuses.

The review methodology of the paper aims to present various energy sources for different types of campus microgrids. This will also facilitate researchers in their respective areas and optimize the microgrid with the updated energy management systems [46]. The methodology monitored the power flow information in real time, monitored energy consumption, and stabilized the campus microgrid’s energy [47]. It also covered a timeline of 5 years of technological development, including aspects from 2014 up to the latest microgrid developments. It also provides a new solution for a microgrid that operates for different power plants. This paper discusses various power plants and microgrids’ architectural designs, techniques, operations, and reliability. These were analyzed with many optimization algorithms, fuzzy logic algorithms, and ANNs (artificial neural networks) [48].

This paper delivers the literature review on the campus microgrid EMSs by classifying the remaining articles into the following categories:

57
]
Al-Akhawayn campus, Morocco RER 1*

Smart meters

Sensors		 Energy management system Minimize energy losses and GHG emissions
[9] Sebelas Maret University, Indonesia RER

Solar–PV

Energy Storage		 HOMER analysis NPC cost: USD 153,730

IRR value: 4.9%
Fuzzy logic is commonly used in spacecraft, automotive industries, traffic control, and especially in improving the efficiency of the transmission system.
[
86] A comparative scenario is explained to use RERs (renewable energy resources) in almost 50 universities as sample case studies worldwide. In this paper, three different approaches were developed to optimize the university microgrid, in which many macro-, medium- (meso), and macro-level cases were discussed.
[62] Jordan University of Science and Technology, Irbid, Jordan PV plant

Utility grid		 Charging/discharging algorithm
Other Methods
[92]Manta Ray Optimization [82]Reduce the energy consumption from Computational cost is comparatively less compared to other optimizers and also has good precision in solutions.

622.4 MWh to 6.3.87 MWh
It is not effective in fine-tuning for providing solutions for optima, and it has a slow convergence speed, making it less usable. The manta ray technique is a bio-inspired optimization technique idealized from the excellent behavior of large manta rays, which are known for their speed. It is widely used for its solution precision and computational cost. The latest research is reviewed in the literature on DERs (distributed energy resources), which aim to train students with in latest courses of microgrid technologies. This project was undertaken as a MERMET Project, which over the lifespan has trained almost 11,012 students with 154,432 credit hours lectured to trainees. [63] METU (Middle East Technical University) campus and NCC (Northern Cyprus Campus) RES

ESS
[Generalized reduced gradient (GRG) algorithm 93]Increased the RES fraction by 91.8%

Demand and supply fraction by 89.4%

COE calculated 6.175 USD per kWh
Harris hawks Optimization [83] Commonly known for its excellent performance, acceptable convergence, and quality of results generated for optimization problems. The GridEd project is discussed among seven universities based in different cities. This GridEd project aims to modernize the education curriculum with improved training for future engineers.Sometimes difficult to understand and has computation complexity, which makes it more difficult. HHO is in the initial stages for researchers, and it has acceptable convergence, accuracy, and speed for solving various optimization problems in the real world. [64] Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
[94]Grid

Battery		 Forecasting method Reduces the peak energy consumption by 11%–32% and saves USD 496,320 annually
A solution is presented for the Santa Rita Jail in which a microgrid is installed 70 km away from the current operating location. [13] Chonnam National University Yongbong Campus, Gwangju, South Korea 500 kW ESS

PV

Load controllers

Power load-bank		 P2P trading mechanism
[95Maximized the performance of every interlinked microgrid
] An EMS system is presented for the University of Genova, Savona campus, which aims to effectively manage the energy, reducing the generation costs of the smart polygeneration grid. [65] Guangdong University of Technology, China BESS

PV system		 NSGA-2 (Non-dominated Sorting Genetic Algorithm-2) To maximum PV consumption and to

minimize the operational cost
[96] An analysis is developed to improve the power demand for Gachon University, South Korea. It consists of distributed energy resources with an energy storage system. The system improves the efficiency and sustainability of the university microgrid. [66] Nanjing University, China EV 2*

 ✓ ✗ ✗ ✗ ✗
[58] Purdue University, Indiana, USA Solar–PV grid

3 lead–acid batteries		 EMS technique Annual ROI: USD 602.88 Payback period: 13.38 years.
[11] Eindhoven University of Technology, The Netherlands RES

Distributed Generators

Storage systems		 Generic algorithm✗ ✗ Campus

Building
[27] Seoul University, South Korea ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus
[28] Griffith University, Australia ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus
[29] Federal University of Rio de Janeiro, Brazil ✓ ✓ ✓ ✗ ✓ ✓ ✗ ✗ ✗ ✗ Campus
[30] University of Southern California, USA ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ Campus
400 kWh energy production
[59] (Illinois Institute of Technology), Chicago, USA (DERs)

(DG)

(ES) resources		 Energy scheduling optimization problem (ESOP) Power balance✗ ✗ ✓ ✗ Campus

Building
[31] Nanyang Technological University (NTU), Singapore ✓ ✓ ✗ ✗ ✗ ✗ ✓ ✗ ✗ ✗ Campus
[32] Illinois Institute of Technology, USA ✓ ✓ ✓ ✗ ✓ ✓ ✗ ✗ ✗ ✗ Campus
[33] Eindhoven University of Technology, The Netherlands ✓ Wind system✓ ✗ ✗

PV✗		 ✗ ✗ ✗ ✗ ✗ Campus
Interval optimization Transmission loss is reduced [34] Al-Akhawayn University, Morocco
[67✓ ] Multiple Microgrids location such as Nanjing University Microgrid (PV)

Wind turbines

Energy storage units

(EV)

Diesel generators

Gas turbine		 OPF (optimal power flow) technique

Auction algorithm

CPLEX solver		 Achieved a minimal USD 8616 operation cost [35] University of Genova, Savona Campus, Italy ✗ ✓ ✗ ✗ ✗ ✗ ✓ ✗ ✗ ✗ Campus

Building
[68] University of Connecticut, Mansfield, Connecticut, USA Wind turbine

Fuel cell

PV

Energy storage system

Hydro-kinetic systems		 HOMER analysis The final selected microgrid consisted of solar–PV (203,327 kW), wind turbine system (225,000 kW), and energy storage systems (730,968 kWh) [36] University of Central Missouri, USA ✓ ✓ ✓ ✗
[69] Nnamdi Azikiwe University, Nigeria Solar–PV

Diesel generator		 HOMER analysis✗ ✗ ✓ ✓ ✗ ✗ Campus
The NPV and LCOE were calculated as USD 1,738,994 and USD 0.264 [37] Yuan Ze University, Taiwan ✗
[70✗ ✗ ✗ ✗ ✗ ] McNeese State University, Lake Charles, Louisiana, USA CHP

✓		 ✗ ✗ NG microturbine✗

Campus

Building
PV plant HOMER analysis A CHP-PV-based hybrid system is efficient [14] Chalmers University of Technology, Sweden
[✓ ✓ ✗ ✗ ✗ ✗ ✗ 71✗ ] University of Coimbra, Portugal PV ✗ 3* plant

Li-ion batteries

EV✗

Campus
Controllers LabVIEW analysis Lower energy consumption and it met electricity demand for the campus by 22.3% yearly [38] Federal University of Pará, Brazil ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗
[72] Proposed University based in IndiaCampus
Wind system

PV system

Energy storage

Biomass		 Newton–Raphson technique

Swarm intelligence approach		 It improved the energy exchange among grids, and also enhanced power quality [39] Clemson University, South Carolina ✓ ✓ ✗ ✗ ✓ ✗ ✗ ✗ ✗ ✗ Campus
[40] University of Connecticut, Mansfield, Connecticut, USA ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus Multiple

Buildings
[41] University of Science and Technology, Algeria ✓ ✕ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus
[42] University of Wisconsin-Madison, USA ✓ ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗ ✗ Campus
[43] De Vega Zana, Spain ✓ ✗ ✗ ✗ ✗ ✗ ✗ ✗
  • Campus microgrids: optimization techniques;
  • Renewable energy utilization in campus microgrids;
  • Modeling techniques of campus microgrids;
  • Resilient power system using campus microgrid;
  • Role of energy storage systems in campus microgrids;
  • Simulation tools for campus microgrids.

2. Campus Microgrids: Optimization Techniques

Campus microgrids’ energy management involves some automatic systems that aim to schedule the resources optimally. It comprises the latest information technology, manages the energy storage system, and distributes energy sources with optimum conditions. Campus microgrid optimization typically involves the following points to improve the generator power to the maximum value and reduce the microgrid’s operation cost and environmental cost. The main objective of the optimization techniques is to increase the efficiency of the power system [49].

Many standard optimization methods include mixed-integer linear programming (MILP) and non-linear programming [50]. Well-known deterministic mathematical methods are MILP, MILNP, and dynamic programming, which deal with and resolve the complications quickly and comprehensively, whereas metaheuristic mathematical models [

1* RER denotes renewable energy resources. 2* EV denotes electric vehicle. 3* PV denotes photovoltaic.

Various optimization methods have also been applied to improve power efficiency, reduce electricity costs, and take full advantage of improved storage systems [73]. Various researchers have applied multiple methods such as MILP, dynamic programming, MINLP, particle swarm optimization (PSO), genetic algorithms (GAs), an artificial bee colony, artificial fish swarm, and bacterial foraging algorithm. MILP is the latest to be implemented into microgrid systems, similarly to the other latest techniques such as artificial neural networks, artificial intelligence, or machine learning. The assurance of searching for the global optimal point in the linear problem makes the MILP method more attractive among commercial solvers, and its limitation is the impossibility of dealing with the nonlinear effects and the main risk of facing a high-dimensionality problem. Several other methods have been established to challenge these types of limitations, such as rolling horizon methods, piecewise linearization approaches, and high-dimensionality reduction by clustering algorithms. Dynamic programming, on the other hand, splits the problems into their following parts and then finds the optimal solution; its main advantage is the computational saving over complete enumerations, but it also has high-dimensionality problems similarly to MILP because it faces issues in dealing with multiple states. MINLP, on the other hand, solves problems with simple operations and contains many optimal solutions that take positive benefits over MILP because it also deals with nonlinearity optimization problems. However, the main problem is that it performs more complex iterations than MILP and is hard to understand. Particle swarm optimization (PSO), developed in 1995 by Kennedy and Eberhart, has greater efficiency than MILNP, but it has complex computation while solving an optimization problem. Genetic algorithms, on the other hand, developed in 1975, support multi-objective optimization, but the usage of population size, the finding of the main parameters such as the rate of mutation and crossover, and the choices of the new population should be made carefully.

Many researchers have used these algorithms to find optimal solution, as seen in Table 3.

Table 3. Comparison of optimization methods considering advantages and disadvantages.

Methods

3. Renewable Energy Utilization in Campus Microgrids

Microgrids have progressed as a critical technology to aggregate and harness the existing renewable energy sources (RESs) to increase network reliability, reduce energy costs, and reduce the carbon footprint. Many researchers have discussed renewable energy utilization among various campus microgrids by employing different approaches and methods. However, all methods have focused on determining the most efficient and optimal solution for the microgrid operation. The respective sub-sections discuss the renewable energy utilization among various grids.

Navid presents an optimal solution in [84] to optimize the system’s size and reduce the net present cost (NPV) for a campus grid that consists of PV, wind, integrated converters, and a BESS system. The university grid was connected with 100% RERs to reduce the LCOE (levelized cost of energy) by half that of an urban university which buys electricity from a utility company. The proposed RES also included a tracker system, which aimed to reduce the LCOE by a further 50%, although an economic analysis needs to be performed to better understand the COE for the campus.

A DSS (decision support system) was developed by Vangelis in [85] to manage the energy power flow for three cities in Spain, Italy, and The Netherlands. This approach scheduled the temperature set-points, ON/OFF heating system, and PV system for the public and private sectors. The results conclude that it maximized RER production by 10% with cost minimization and reduced GHG (greenhouse gas) emissions, but an optimal solution needs to be focused on by regulating the power quality for the respective cities. Moreover, a more developed DSS approach is needed to effectively manage the load data. On the other hand, a comparative scenario is presented by Walter in [86] to use RER types in nearly 50 universities worldwide. In this study, three different approaches were developed to optimize the university microgrid, in which macro-, medium- (meso), and macro-level cases have been discussed. Universities consume electricity at a maximum of 700 kWh/m2, with a 20 kWh/m2 average consumption level among many universities. Results revealed that 70% of PV/solar renewable energy is a vital source among different universities, but an effective solution needs to be implemented to reduce the operational cost for the university energy, such as with a high penetration of RERs in the campus.

A harmonic model was developed by Alessandro in [87] for the University of Genova, Savona Campus Microgrid, to overcome the transient-based issues present in the system. This proposed model can control and manage the microgrid that is connected with RERs in grid-connected mode and islanded connected mode, but it can also reduce the transients by adding or subtracting generation assets, generator inertia, adding electricity storage, or with dedicated demand response. However, different university microgrids are reviewed in [88], in which UCM is implemented to reduce the operating cost of RESs (renewable energy sources). UCM aims to find an effective techno-economic analysis for other university microgrids. For Jiangan University, 8.3 MW and 11.6 MW are installed on rooftops, covering 33% to 46% of load demand from the PV system. In another scenario, the DUTH University Microgrid gives an annual saving of EUR 8258. The results showed that the increased photovoltaic solar capacity reduced the campus’s energy consumption by 2.8% annually. Moreover, it can represent an effective economic solution for the given universities. An effective approach can also be utilized, such as a smart support solution for campuses to further reduce energy costs.

In [63], Murat suggests a sizing method of renewable energy sources and ESSs (energy storage systems) for the METU (Middle East Technical University) campus and NCC (Northern Cyprus Campus) Microgrid. This methodology compares the performance of the PV system and wind energy system by technical–economic feasibility analysis under four ESS conditions:

(1) No ESS conditions;
(2) HFC (hydrogen fuel cell) conditions;
(3) PHS (pumped hydrogen storage) conditions;
(4) Combination of PHS and HFC.

This system optimally configures the RES by comparing it with the national grid tariff with the COE (cost of electricity) calculation. Results show that it increases the RES fraction to 91.8% from 62.6%, maximizes the demand and supply fraction to 89.4% from 46.5%, and LCOE is calculated as 6.175 USD per kWh. Furthermore, with COE, the NPC (net present cost) can also be calculated to enhance the technical–economic feasibility analysis, and more conditions should be focused on optimizing the system more economically.

A power-sharing energy market was established by Javad in [89] for commercial buildings in Portugal. These commercial buildings consist of ESS on-site and EV charging stations. The proposed method aims to increase the PV output, which covers the demand profile for the communities. Results show that it maximizes the self-consumption of renewable energy for commercial buildings and communities and reduces the total electricity cost by 27%. However, to increase the self-consumption of renewable energy, more resources should be incorporated to further move towards RER self-consumption with the consideration of techno-economic analysis.

An efficient microgrid system is presented by Reyasudin in [90] that included solar–PV and BESS, which were implemented in HOMER software. The battery storage system had various load ranges connected with µG that had a range of 1 kW to 500 kW capacity. Results show that both the systems, grid-connected only and grid-connected with battery storage, are feasible. Both can be used at the proposed campus. An approach also needs to be focused on analyzing the BESS optimal sizing, which could be the best possible solution for the microgrid system.

Dimitrios developed another approach in [91] to deal with the electric power losses in a microgrid with optimal scheduling of generating resources. The two-stages platform devised this integrated approach. At first, it used an EMS approach (energy management system) that calculated the system’s economic dispatch and load dispatch. The second stage applied a strategy to meet the standards of power quality at the distribution side level. The results showed that this tool effectively used voltage regulation, unbalancing conditions, and harmonics deviations with the minimum cost. The system can also focus on frequency regulation. If demand-side frequency response is focused, then it will mitigate problems for the power losses.

However, the related work focused here presents a review on renewable energy utilization for multiple microgrids, presenting solutions to improve network reliability, reduce the cost of energy, and reduce the carbon footprint. Many researchers have also investigated efficient and optimal solutions for the microgrid operation; however, this manuscript gives appropriate solutions for the campus microgrids which are approaches that can be further utilized to improve the reliability and sustainability for power systems to give customers a good level of confidence that the provided solution is effective.

Therefore, this study focused on the recent literature on campus microgrids that also covered a brief comprehensive analysis of the different microgrid models worldwide with the techniques and energy systems used.

A comparison has been developed to better analyze existing review papers and our survey paper, as shown in Table 4.

Table 4. Comparison between existing studies of campus microgrids and our survey paper.

Existing Literature Reviews of Microgrids Objectives
[30] A DR (demand response)-based software architecture is highlighted in the literature to optimize the microgrid of the USC (University of Southern California) campus, LA (Los Angeles). It comprises the data collected under machine learning models to effectively schedule the load demand for peak hours.
[32] A system of the establishment of microgrids is proposed at IIT (Illinois Institute of Technology), Chicago. In this system, reliability, sustainability, and efficiency are concerned.
[33] A smart design of smart grids is proposed for the Eindhoven University of Technology, The Netherlands. It provided some solutions to convert the existing distributed system into an intelligent grid system.
[34] An EMS (energy management system) approach is presented in the literature for Al-Akhawayn University in Morocco, which can efficiently control the energy for this smart microgrid.
[36] A microgrid model is proposed, and a solution is given to handle the UCM campus load, manage the EV (electric vehicle) connections, and mitigate problems related to peak campus demands.
[45] The power management and scheduling problems are addressed in this study with hybrid renewable microgrids in the North China Electric-Power University, Beijing.
[57] An overview is presented for the topics of smart campuses, EMSs (energy management systems), CBSs (control-based systems), and stability solutions for campus microgrids. This paper introduced energy management for the Al-Akhawayn campus microgrid.
Current survey paper
In the current survey paper, the main objective is to organize, review, and present a comparative analysis of all the existing campus microgrid systems with the consideration of multiple optimization techniques, simulation tools, and different types of energy storage technologies.

References

  1. Vera, Y.E.G.; Dufo-López, R.; Bernal-Agustín, J.L. Energy management in microgrids with renewable energy sources: A literature review. Appl. Sci. 2019, 9, 3854.
  2. Environment America Editor SUNY New Paltz Combines Clean Energy and Energy Storage to Avoid Blackouts. American Website. 2020. Available online: https://environmentamerica.org/energy-101/microgrids-energy-storage (accessed on 7 September 2021).
  3. Silva, M.; Fernandes, F.; Morais, H.; Ramos, S.; Vale, Z. Hour-ahead energy resource management in university campus microgrid. In Proceedings of the 2015 IEEE Eindhoven PowerTech, Eindhoven, The Netherlands, 29 June–2 July 2015; pp. 1–6.
  4. Machamint, V.; Oureilidis, K.; Venizelou, V.V.; Efthymiou, G.E. Georghiou Optimal energy storage sizing of a microgrid under different pricing schemes. In Proceedings of the 2018 IEEE 12th International Conference Compatibility Power Electronics and Power Engineering, CPE-POWERENG, Doha, Qatar, 10–12 April 2018; pp. 1–6.
  5. Hossain, A.; Pota, H.R.; Squartini, S.; Zaman, F.; Guerrero, J.M. Energy scheduling of community microgrid with battery cost using particle swarm optimisation. Appl. Energy 2019, 254, 113723.
  6. Hadjidemetriou, L.; Zacharia, L.; Kyriakides, E.; Azzopardi, B.; Azzopardi, S.; Mikalauskiene, R.; Al-Agtash, S.; Al-hashem, M.; Tsolakis, A.; Ioannidis, D.; et al. Design factors for developing a university campus microgrid. In Proceedings of the 2018 IEEE International Energy Conference, ENERGYCON 2018, Limassol, Cyprus, 3–7 June 2018; pp. 1–6.
  7. Savić, N.S.; Katić, V.A.; Katić, N.A.; Dumnić, B.; Milićević, D.; Čorba, Z. Techno-economic and environmental analysis of a microgrid concept in the university campus. In Proceedings of the 2018 International Symposium on Industrial Electronics, INDEL 2018, Banja Luka, Bosnia and Herzegovina, 1–3 November 2018.
  8. Bracco, S.; Delfino, F.; Laiolo, P.; Rossi, M. The Smart City Energy infrastructures at the Savona Campus of the University of Genoa. In Proceedings of the AEIT 2016—International Annual Conference on Sustainable Development, Capri, Italy, 5–7 October 2016.
  9. Kristiawan, R.B.; Widiastuti, I.; Suharno, S. Technical and economical feasibility analysis of photovoltaic power installation on a university campus in Indonesia. MATEC Web Conf. 2018, 197, 1–5.
  10. Galvin, R.W. Microgrid Project at IIT (Illionois Institue Technology); Illionois Institue Technology: Chicago, IL, USA, 2019; p. 1.
  11. Lorincz, J.; Bule, I.; Kapov, M. Performance analyses of renewable and fuel power supply systems for different base station sites. Energies 2014, 7, 7816–7846.
  12. Bertolotti, V.; Procopio, R.; Rosini, A.; Bracco, S.; Delfino, F.; Soh, C.B.; Cao, S.; Wei, F. Energy Management System for Pulau Ubin Islanded Microgrid Test-bed in Singapore. In Proceedings of the 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Madrid, Spain, 9–12 June 2020.
  13. Kim, B. Implementation and optimal operation of campus microgrid-ems system considering multi-mg power trading. In Proceedings of the 25th International Conference on Electricity Distribution, Madrid, Spain, 3–6 June 2019; pp. 3–6.
  14. Goransson, M.; Larsson, N.; Tuan, L.A.; Steen, D. Cost-benefit analysis of battery storage investment for microgrid of Chalmers university campus using μ-OPF framework. In Proceedings of the 2017 IEEE Manchester PowerTech, Powertech 2017, Manchester, UK, 18–22 June 2017.
  15. Hassan, M.A.S.; Chen, M.; Lin, H.; Ahmed, M.H.; Khan, M.Z.; Chughtai, G.R. Optimization modeling for dynamic price based demand response in microgrids. J. Clean. Prod. 2019, 222, 231–241.
  16. Mazzola, S.; Astolfi, M.; Macchi, E. A detailed model for the optimal management of a multigood microgrid. Appl. Energy 2015, 154, 862–873.
  17. Tenfen, D.; Finardi, E.C. A mixed integer linear programming model for the energy management problem of microgrids. Electr. Power Syst. Res. 2015, 122, 19–28.
  18. Krishnan, A.; Sampath, L.P.M.I.; Eddy, Y.S.F.; Gooi, H.B. Optimal Scheduling of a Microgrid Including Pump Scheduling and Network Constraints. Complexity 2018, 2018, 9842025.
  19. Aghajani, G.R.; Shayanfar, H.A.; Shayeghi, H. Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers. Manag. 2015, 106, 308–321.
  20. Raza, A.; Malik, T.N. Energy management in commercial building microgrids. J. Renew. Sustain. Energy 2019, 11, 015502.
  21. Shayeghi, H.; Shahryari, E.; Moradzadeh, M.; Siano, P. A Survey on Microgrid Energy Management Considering Flexible Energy Sources. Energies 2019, 12, 2156.
  22. Silva, V.A.; Aoki, A.R.; Lambert-Torres, G. Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System. Energies 2020, 13, 5188.
  23. Antoniadou-Plytaria, K.; Steen, D.; Tuan, L.A.; Carlson, O. Energy Scheduling Strategies for Grid-Connected Microgrids: A Case Study on Chalmers Campus. In Proceedings of the 2019 IEEE PES Innovative Smart Grid Technologies Europe ISGT-Europe 2019, Bucharest, Romania, 29 September–2 October 2019; pp. 1–5.
  24. Chedid, R.; Sawwas, A.; Fares, D. Optimal design of a university campus micro-grid operating under unreliable grid considering PV and battery storage. Energy 2020, 200, 117510.
  25. För, S.; Och, E.; Carneheim, S. Energy Storage System for Local Generation in a Grid-Connected Microgrid: Sizing and Analyzing an Energy Storage System for the Tezpur University Campus. Master’s Thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 2020.
  26. Let, D.; Tene, B.I.; Husu, A.G.; Stan, M.F.; Stancu, L.M.; Let, A. Feasibility of a micro grid scale up at campus level—Case study. In Proceedings of the 11th International Conference on Electronics, Computers and Artificial Intelligence ECAI 2019, Pitesti, Romania, 27–29 June 2019.
  27. Husein, M.; Chung, I.-Y. Optimal design and financial feasibility of a university campus microgrid considering renewable energy incentives. Appl. Energy 2018, 225, 273–289.
  28. Leskarac, D.; Moghimi, M.; Liu, J.; Water, W.; Lu, J.-W.; Stegen, S. Hybrid AC/DC Microgrid testing facility for energy management in commercial buildings. Energy Build. 2018, 174, 563–578.
  29. Bellido, M.H.; Rosa, L.P.; Pereira, A.O.; Falcão, D.M.; Ribeiro, S.K. Barriers, challenges and opportunities for microgrid implementation: The case of Federal University of Rio de Janeiro. J. Clean. Prod. 2018, 188, 203–216.
  30. Simmhan, Y.; Prasanna, V.; Aman, S.; Natarajan, S.; Yin, W.; Zhou, Q. Toward data-driven demand-response optimization in a campus microgrid. In Proceedings of the BuildSys 2011—The 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency Buildings, Seattle, WA, USA, 1 November 2011; pp. 41–42.
  31. Kumar, K.N.; Sivaneasan, B.; So, P.L.; Gooi, H.B.; Jadhav, N.; Singh, R.; Marnay, C. Sustainable Campus with PEV and Microgrid. Proc. ACEEE Summer 2012, 11, 128–139.
  32. Shahidehpour, M. Summary of a panel presentation at the 2011 general meeting of IEEE/PES: Establishment of campus microgrid for power engineering education and research. In Proceedings of the IEEE Power Energy Society General Meeting, Detroit, MI, USA, 24–28 July 2011; p. 1.
  33. Gonzalez, R.M.; van Goch, T.A.J.; Aslam, M.F.; Blanch, A.; Ribeiro, P.F. Microgrid design considerations for a smart-energy university campus. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference Europe, Istanbul, Turkey, 12–15 October 2014; Volume 2015, pp. 1–6.
  34. Talei, H.; Zizi, B.; Abid, M.R.; Essaaidi, M.; Benhaddou, D.; Khalil, N. Smart campus microgrid: Advantages and the main architectural components. In Proceedings of the 2015 IEEE International Renewable and Sustainable Energy Conference IRSEC, Marrakech, Morocco, 10–13 December 2015; pp. 1–7.
  35. Brenna, M.; Foiadelli, F.; Longo, M.; Bracco, S.; Delfino, F. Smart microgrids in smart campuses with electric vehicles and storage systems: Analysis of possible operating scenarios. In Proceedings of the IEEE 2nd International Smart Cities Conference, ISC2, Trento, Italy, 12–15 September 2016; pp. 1–6.
  36. Dagdougui, H.; Dessaint, L.; Gagnon, G.; Al-Haddad, K. Modeling and optimal operation of a university campus microgrid. In Proceedings of the IEEE Power and Energy Society General Meeting, Boston, MA, USA, 17–21 July 2016; Volume 2016, pp. 1–5.
  37. Yang, N.C.; Tseng, W.C.; Hsieh, T.Y. Evaluation of maximum installed capacity of electric scooter charging station for microgrid in Yuan Ze University campus. In Proceedings of the 2016 IEEE 5th Global Conference on Consumer Electronics, GCCE 2016, Kyoto, Japan, 11–14 October 2016; pp. 1–2.
  38. Angelim, J.H.; Affonso, C.M. Energy management on university campus with photovoltaic generation and BESS using simulated annealing. In Proceedings of the 2018 IEEE Texas Power Energy Conference TPEC 2018, College Station, TX, USA, 8–9 February 2018; Volume 2018, pp. 1–6.
  39. Arzani, A.; Boshoff, S.; Arunagirinathan, P.; Maigha; Enslin, J.H. System Design, Economic Analysis and Operation Strategy of a Campus Microgrid. In Proceedings of the 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems PEDG 2018, Charlotte, NC, USA, 25–28 June 2018; pp. 1–7.
  40. Kou, W.; Bisson, K.; Park, S.Y. A Distributed Demand Response Algorithm and its Application to Campus Microgrid. In Proceedings of the Conference Record. 3rd IEEE International Workshop on Electronic Power Grid, eGrid 2018, Charleston, SC, USA, 12–14 November 2018; pp. 1–6.
  41. Bourahla, N.A.; Benghanem, M.; Bouzeboudja, H. Conception and Analysis of a Photovoltaic Microgrid in the USTO Campus. In Proceedings of the 2018 3rd International Conference on Electrical Sciences and Technologies in Maghreb, CISTEM 2018, Algiers, Algeria, 28–31 October 2018; pp. 1–6.
  42. Manur, A.; Sehloff, D.; Venkataramanan, G. EnerGyan: A Portable Platform for Microgrid Education, Research, and Development. In Proceedings of the 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems PEDES 2018, Chennai, India, 18–21 December 2018; pp. 1–6.
  43. De Simón-Martín, M.; Bracco, S.; Rossi, M.; Delfino, F.; González-Martínez, A.; Blanes-Peiró, J.J. A flexible test-bed pilot facility for the analysis and simulation of Smart Microgrids. In Proceedings of the 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Genova, Italy, 11–14 June 2019; pp. 1–5.
  44. Ahmad, F.; Alam, M.S. Optimal Sizing and Analysis of Solar PV, Wind, and Energy Storage Hybrid System for Campus Microgrid. Smart Sci. 2018, 6, 150–157.
  45. Liu, Z.; Chen, C.; Yuan, J. Hybrid Energy Scheduling in a Renewable Micro Grid. Appl. Sci. 2015, 5, 516–531.
  46. Mundigler, F. Microgrid Project in Vienna: Small Grid, Major Impact. Siemen Website. 2020. Available online: https://new.siemens.com/global/en/company/stories/infrastructure/2020/microgrid-project-in-vienna.html (accessed on 7 September 2021).
  47. Dongshin University. Dongshin University Smart Energy Campus—Microgrid PRJ. 2020. Available online: http://www.nuritelecom.com/service/micro-grid.html (accessed on 7 September 2021).
  48. Yavuz, L.; Önen, A.; Muyeen, S.; Kamwa, I. Transformation of microgrid to virtual power plant—A comprehensive review. IET Gener. Transm. Distrib. 2019, 13, 1994–2005.
  49. Cui, S.; Wang, Y.-W.; Xiao, J.-W.; Liu, N. A Two-Stage Robust Energy Sharing Management for Prosumer Microgrid. IEEE Trans. Ind. Inform. 2019, 15, 2741–2752.
  50. Muqeet, H.A.U.; Ahmad, A. Optimal Scheduling for Campus Prosumer Microgrid Considering Price Based Demand Response. IEEE Access 2020, 8, 71378–71394.
  51. Niyomubyeyi, O.; Sicuaio, T.E.; González, J.I.D.; Pilesjö, P.; Mansourian, A. A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning. Algorithms 2020, 13, 16.
  52. Helmi, A.M.; Carli, R.; Dotoli, M.; Ramadan, H.S. Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization. IEEE Trans. Autom. Sci. Eng. 2021, 1–17.
  53. Patra, S.B.; Mitra, J.; Ranade, S.J. Microgrid architecture: A reliability constrained approach. In Proceedings of the 2005 IEEE Power Engineering Society General Meeting, San Francisco, CA, USA, 16 June 2005; pp. 2372–2377.
  54. Baron, C.; Al-Sumaiti, A.S.; Rivera, S. Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling. Energies 2020, 13, 957.
  55. Parvini, Y.; Vahidi, A.; Fayazi, S.A. Heuristic Versus Optimal Charging of Supercapacitors, Lithium-Ion, and Lead-Acid Batteries: An Efficiency Point of View. IEEE Trans. Control. Syst. Technol. 2018, 26, 167–180.
  56. Chen, H. A Microgrid Design Case Study: Synchrophasor Placement and Development of a Protection Laboratory for the Oregon State University—Corvallis Campus. J. Chem. Inf. Model. 2019, 53, 1689–1699.
  57. Ameur, A.; Loudiyi, K.; Khallaayoun, A.; Aggour, M.; Mentesidi, K. Dynamic modelling of a solar PV plant: Study case: Al Akhawayn University in Ifrane, Morocco. In Proceedings of the 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC), Marrakech, Morocco, April 2016; pp. 2380–7393.
  58. Wilfing, M.S. Integration of Solar Microgrids. Master’s Thesis, Purdue University, West Lafayette, IN, USA, 2019.
  59. Nigam, S. Quantification of Benefits of the Campus Grid Operations as a Microgrid. 2017. Available online: https://www.ideals.illinois.edu/bitstream/handle/2142/97781/NIGAM-THESIS-2017.pdf?sequence=1 (accessed on 7 September 2021).
  60. Khan, K.R.; Siddiqui, M.S.; Al Saawy, Y.; Islam, N.; Rahman, A. Condition Monitoring of a Campus Microgrid Elements using Smart Sensors. Procedia Comput. Sci. 2019, 163, 109–116.
  61. Iqbal, F.; Siddiqui, A.S. Optimal configuration analysis for a campus microgrid—A case study. Prot. Control. Mod. Power Syst. 2017, 2, 23.
  62. AbuElrub, A.; Hamed, F.; Saadeh, O. Microgrid integrated electric vehicle charging algorithm with photovoltaic generation. J. Energy Storage 2020, 32, 101858.
  63. Al-Ghussain, L.; Samu, R.; Taylan, O.; Fahrioglu, M. Sizing renewable energy systems with energy storage systems in microgrids for maximum cost-efficient utilization of renewable energy resources. Sustain. Cities Soc. 2020, 55, 102059.
  64. Soman, A.; Trivedi, A.; Irwin, D.; Kosanovic, B.; McDaniel, B.; Shenoy, P. Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids. In Proceedings of the e-Energy 2020, The 11th ACM International Conference on Future Energy Systems, Melbourne, Australia, 22–26 June 2020; pp. 237–241.
  65. Zhou, N.; Liu, N.; Zhang, J.; Lei, J. Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response. Energies 2016, 9, 591.
  66. Huang, C.; Yue, D.; Deng, S.; Xie, J. Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization. Energies 2017, 10, 339.
  67. BKim, B.; Bae, S.; Kim, H. Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids. Energies 2017, 10, 566.
  68. Macdonald, S. An Assessment of Renewable Energy Technology Implementation in Storrs, Connecticut: Emissions Reduction and Feasibility of a Microgrid System at UConn. Honor Scholar Thesis, University of Connecticut, Mansfield, UK, 2020.
  69. Ajuzie Uchechukwu, C.; Azubogu, A.C.O.; Inyiama, H.C. Modeling and Simulation of Campus Solar-Diesel Hybrid Microgrid using HOMER Grid (SPGS Nnamdi Azikiwe University Awka Campus as a Case Study). Int. J. Eng. Sci. 2019, 8, 97–108.
  70. Masrur, H.; Khan, K.R.; Abumelha, W.; Senjyu, T. Efficient Energy Delivery System of the CHP-PV Based Microgrids with the Economic Feasibility Study. Int. J. Emerg. Electr. Power Syst. 2020, 21, 1–16.
  71. Moura, P.; Correia, A.; Delgado, J.; Fonseca, P.; De Almeida, A. University Campus Microgrid for Supporting Sustainable Energy Systems Operation. In Proceedings of the 2020 IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference (I&CPS), Las Vegas, NV, USA, 29 June–28 July 2020.
  72. Saritha, K.S.; Sreedharan, S.; Anand, S.R.; Nair, U. Swarm Intelligence Approach for Optimum Renewable Integration of Campus Microgrid—A Case Study. GMSARN Int. J. 2021, 15, 157–165.
  73. Kumar, A.; Singh, A.R.; Deng, Y.; He, X.; Kumar, P.; Bansal, R.C. Multiyear Load Growth Based Techno-Financial Evaluation of a Microgrid for an Academic Institution. IEEE Access 2018, 6, 37533–37555.
  74. Gao, H.-C.; Choi, J.-H.; Yun, S.-Y.; Lee, H.-J.; Ahn, S.-J. Optimal Scheduling and Real-Time Control Schemes of Battery Energy Storage System for Microgrids Considering Contract Demand and Forecast Uncertainty. Energies 2018, 11, 1371.
  75. Mitra, J.; Patra, S.B.; Ranade, S.J. A dynamic programming based method for developing optimal microgrid architectures. In Proceedings of the 15th Power Systems Computational Conference PSCC 2005, Liege, Belgium, 22–26 August 2005; pp. 22–26.
  76. Liu, W.; Liu, C.; Lin, Y.; Bai, K.; Ma, L.; Chen, W. Multi-Objective Optimal Scheduling Method for a Grid-Connected Redundant Residential Microgrid. Processes 2019, 7, 296.
  77. Panda, S.; Yegireddy, N.K. Multi-Input Single Output SSSC based damping controller design by a hybrid Improved Differential Evolution-Pattern Search approach. ISA Trans. 2015, 58, 173–185.
  78. Panda, S.; Padhy, N.P. Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Appl. Soft Comput. J. 2008, 8, 1418–1427.
  79. Neshat, M.; Sepidnam, G.; Sargolzaei, M.; Toosi, A.N. Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 2014, 42, 965–997.
  80. Husein, M.; Chung, I.-Y. Day-ahead solar irradiance forecasting for microgrids using a long short-term memory recurrent neural network: A deep learning approach. Energies 2019, 12, 1856.
  81. Mahmoud, T.S.; Habibi, D.; Bass, O. Fuzzy logic for smart utilisation of Storage Devices in a typical microgrid. In Proceedings of the 2012 International Conference on Renewable Energy Research and Applications (ICRERA), Nagasaki, Japan, 11–14 November 2012.
  82. Alturki, F.A.; Farh, H.M.H.; Al-Shamma’A, A.A.; Alsharabi, K. Techno-economic optimization of small-scale hybrid energy systems using manta ray foraging optimizer. Electronics 2020, 9, 2045.
  83. Yousri, D.; Babu, T.S.; Fathy, A. Recent methodology-based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants. Sustain. Energy Grids Netw. 2020, 22, 100352.
  84. Shirzadi, N.; Nasiri, F.; Eicker, U. Optimal Configuration and Sizing of an Integrated Renewable Energy System for Isolated and Grid-Connected Microgrids: The Case of an Urban University Campus. Energies 2020, 13, 3527.
  85. Marinakisa, V.; Doukasa, H.; Tsapelasa, J.; Mouzakitisa, S.; Sicilia, Á.; Madrazob, L.; Sgouridisc, S. From big data to smart energy services: An application for intelligent energy management. Futur. Gener. Comput. Syst. 2020, 110, 572–586.
  86. Filho, W.L.; Salvia, A.L.; Paço, A.D.; Anholon, R.; Quelhas, O.L.G.; Rampasso, I.S.; Ng, A.; Balogun, A.-L.; Kondev, B.; Brandli, L.L. A comparative study of approaches towards energy efficiency and renewable energy use at higher education institutions. J. Clean. Prod. 2019, 237, 117728.
  87. Labella, A.; Mestriner, D.; Procopio, R.; Delfino, F. A simplified first harmonic model for the Savona Campus Smart Polygeneration Microgrid. In Proceedings of the 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Milan, Italy, 6–9 June 2017; pp. 1–6.
  88. Akindeji, R.; Tiako, I.E. Davidson Use of renewable energy sources in university campus microgrid—A review. In Proceedings of the 27th International Conference on the Domestic Use of Energy DUE, Wellington, South Africa, 25–27 March 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 76–83.
  89. Moura, P.; Sriram, U.; Mohammadi, J. Sharing Mobile and Stationary Energy Storage Resources in Transactive Energy Communities. arXiv 2020, arXiv:2008.08971. Available online: http://arxiv.org/abs/2008.08971 (accessed on 7 September 2021).
  90. Khan, M.R.B.; Pasupuleti, J.; Al-Fattah, J.; Tahmasebi, M. Optimal Grid-Connected PV System for a Campus Microgrid. Indones. J. Electr. Eng. Comput. Sci. 2018, 12, 899–906.
  91. Thomas, D.; D’Hoop, G.; Deblecker, O.; Genikomsakis, K.; Ioakimidis, C. An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes. Appl. Energy 2020, 260, 114314.
  92. The Midamerica Consortium. Microgrid DE-EE0006341 The MidAmerica Regional Microgrid Education and Training (MARMET) Consortium Missouri University of Science and Technology. Available online: https://www.osti.gov/servlets/purl/1485448 (accessed on 7 September 2021).
  93. EPRI . Leveraging Industry Research to Educate a Future Electric Grid Workforce. 2019; p. 32. Available online: https://www.osti.gov/servlets/purl/1529977 (accessed on 7 September 2021).
  94. Marnay, C.; Deforest, N.; Lai, J. A green prison: The Santa Rita Jail campus microgrid. In Proceedings of the IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 22–26 July 2012; pp. 1–2.
  95. Bracco, S.; Brignone, M.; Delfino, F.; Procopio, R. An energy management system for the savona campus smart polygeneration microgrid. IEEE Syst. J. 2015, 11, 1799–1809.
  96. Lee, Y.; Kim, J. Analysis of the Campus Microgrid Power Demands. Energy Syst. 2015, 461–462. Available online: https://www.koreascience.or.kr/article/CFKO201531751953500.pdf (accessed on 7 September 2021).
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