# Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges

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## Abstract

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## 1. Introduction

- This paper focuses on the survey of optimal scheduling of the distributed energy resources with the various campus microgrids;
- It also presents the scheduling of different energy resources with a comprehensive review of the energy management of various campus microgrids at different locations;
- EMS of microgrid has been reviewed considering the distributed generation, renewable energy resources, demand-side management (DSM), and ESS;
- Energy management and optimal scheduling of microgrids have been evaluated concerning objective functions (OFs), optimization techniques, simulation tools, and constraints. A comprehensive research challenges and issues are discussed.

- (1)
- Solar PV;
- (2)
- Wind turbine;
- (3)
- Fuel cell;
- (4)
- Diesel generator;
- (5)
- Energy Storage System.

## 2. Energy Management of Campus Microgrids with Distributed Generations

- With only a grid attached;
- With photovoltaic (PV) source and ESS along with the grid source;
- With Wind energy, PV, and ESS along with the grid source.

#### 2.1. Solar PV in Campus Microgrids

#### 2.2. Wind Turbine in Campus Microgrids

#### 2.3. Fuel Cell (FC) in Campus Microgrids

#### 2.4. Diesel Generator in Campus Microgrids

#### 2.5. Energy Storage System in Campus Microgrids

## 3. Microgrid EMS Objective Functions and Constraints

#### 3.1. Objective Functions

#### 3.2. Constraints

#### 3.3. Uncertainty Parameters

## 4. Multiple Approaches Used for Optimal Scheduling of Campus Microgrids

#### 4.1. Heuristic Approaches

#### 4.2. Multiagent System (MAS)

#### 4.3. Mathematical Methods

#### 4.3.1. CPLEX Solver

#### 4.3.2. SNOPT Solver

#### 4.3.3. Gurobi Optimizer

#### 4.4. Test System of Validation

## 5. Research Challenges

- To maximize the utilization of green sources on campus;
- To minimize the campus microgrid’s operating and running costs as low as possible;
- To ensure the system’s reliability and dependability;
- To reduce the use of utility electricity by offering renewable energy resources;
- To improve the system more stable by incorporating modern optimization techniques;
- To improve an EMS that is meant to maximize the efficiency of the system;
- To ensure electricity unit prices efficient, a better time-of-use pricing scheme is necessary;
- To create an effective economic plan in order to increase the economic benefit of the advanced campus microgrid system.

_{2}reduction, will be critical.

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

Acronyms | |

EMS | Energy management system |

ESS | Energy storage system |

DOD | Depth of discharge |

FIT | Feed-in-Tariffs |

BESS | Battery energy storage system |

BSOC | Battery state of charge |

DG | Distributed generator |

DERs | Distributed energy resources |

DSM | Demand-side management |

GHG | Greenhouse gas |

LP | Linear programming |

PV | Photovoltaic |

MILP | Mixed integer linear programming |

TOU | Time-of-Use |

RERs | Renewable energy resources |

μG | Microgrid |

FLC | Fuzzy logic controller |

SOC | State of charge |

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Ref. | Subject | Components | Optimization Techniques | Load Types | Results |
---|---|---|---|---|---|

[11] | Illinois Institute of Technology (IIT) | Distributed generation (DG), controllable loads, storage, Switch | High-reliability distribution system (HRDS) | Electrical appliances | Annual Operational cost reduces (140,497 $/year. to 126,644 $/year.) |

[11] | Illinois Institute of Technology (IIT) | Distributed generation (DG), controllable loads, storage, Switch | High-reliability distribution system (HRDS) | Electrical appliances | Annual Operational cost reduces (140,497 $/year. to 126,644 $/year.) |

[12] | University Kuala Lumpur, British Malaysian Institute | Photovoltaic (PV), battery storage system, Wind, Converter | Hybrid Optimization Model for Electrical Renewable (HOMER) | Typical load profile for a small campus | Economical evaluation of greenhouse gasses emissions |

[13] | 50 higher universities around the world | All renewable resources, energy storage system | All universities have different Techniques | Electrical load | Economic benefits |

[14] | Nathan Campus, Griffith University, Australia | DG and ESS, battery bank, PV, WT, FC | Control and management system operation | AC DC Load, EV. | Energy management system |

[15] | Nanyang Technological University (NTU), Singapore campus | PV, FC, and Natural-gas operated MT | Laboratory of Clean Energy Research (LaCER) | Buildings and transportation | Microgrid Energy Management System (MG-EMS |

[16] | All Prosumers | ESS, PV, and wind generation | MILP, MICP | Domestic and Commercial Load | Saving in Electrical cost |

[17] | Overview microgrid implementation in American, Asian and European countries. | Control system, Utility network, renewable sources, Diesel generator | Different techniques use | Electrical appliances | Power quality and reduce dependency |

[18] | rural areas | Diesel generator, PV, Energy Storage Battery’s, metering | IBM ILOG CPLEX | Electrical appliances | Efficient |

[19] | Modified Microgrid | Diesel generator, Wind, Microturbine, Energy Storage Battery’s, metering | (GAMOM), (PSO), (TLBO) | Electrical appliances | Economic benefits, less solving time |

[20] | Modified microgrid with the usage of inverter | PV, Fuel cell, inverters | a multiagent system (MAS)-based | Electrical appliances | Reduce Communication |

[21] | Industries | PV, Wind, Energy storage system, Diesel generator | MILP | Industrial load | Economic benefit |

[22] | Islanded residential microgrid (MG) | Gas engine, Microturbine, PV, Fuel Cell, Energy Storage system | Two-stage stochastic programming | Electrical appliances | maximize the expected profit of MG and energy payments of customers. |

[23] | Optimal scheduling Multi microgrid | MT, GE, Wind, PV, Energy storage, Fuel cell | MILP | Electrical load | Most reliably and economical |

[24] | Multi-Microgrids | PV, Wind, ESS, DiG, FC | MILP, CPLEX 11 under GAMS | Electrical load | Minimize the operation costs and optimally schedule energy resources to fulfill the demand loads |

[25] | To enhance the resilience of distribution systems (DS) | PV, Wind, ESS | MILP, Gurobi | EV, Domestic, Commercial Load | It minimizes power system cost, generation cost, and customer interruption cost |

[26] | Multi-Microgrids with ESS | MT, PV, Energy Storage system | bi-level model Optimize Problem, (GAMS) | Electrical load | Reduce the operational cost and maximize the owner profits |

[27] | Grid-Connected Microgrid | PV, Wind, GE, ESS, MT | MINLP, NSGA | Electrical load | It maximizes the profit and reduces the GHG emissions |

[28] | Electrical Thermal resources in microgrid | GE, PV, ESS, Wind, converter, inverter | MILP | Thermal, Electrical load | It minimizes the operation costs |

[29] | AC/DC Hybrid Multi-microgrids | DiG, ESS, PV, Wind | YALMIP toolbox of MATLAB and CPLEX solver 12.4 | Electrical load | Economic benefit |

[30] | scheduling flexible resources in microgrids operation | ESS, PV | MOSEK SOCP | Electrical load | Economic benefit |

Techniques | Optimization Methods | Advantages | Disadvantages | Applications and Objectives |
---|---|---|---|---|

Deterministic Techniques | MILP [46] | The problems are swiftly and completely resolved using mixed-integer linear programming (LP). Their linear constraint is located in the viable convex area, with the goal of locating the best global point and precise solution. | Economic and stochastic analysis are two types of analysis. It has limited capabilities for applications with objective functions that are not continuous or distinct. | For optimization challenges, MILP is often utilized. It’s simple to operate with CPLEX Solver, that is a good piece of software. Unmanned aerial vehicles (UAVs) utilize it to design their flight trajectories. |

Dynamic Programming (DP) [47] | To divide the difficulties into smaller components and then optimizing them to obtain the best answer | It is time-consuming since it has a huge number of recursive routines. | It is also employed as an issue of optimization. It handles issues like dependability design, robots control, and navigation systems, among others. | |

MINLP [27] | Solve issues using basic operations and has a large number of optimum solutions that outperform MILP. | It takes a long time. | Mixed-integer nonlinear programming (MINLP) is a method for solving optimization problems containing continuous and discrete variables in the optimization problem, as well as complex variables. | |

MetaheuristicTechniques | Particle Swarm Optimization (PSO) [48] | Greater productivity while fixing optimization issues. Easy adaption for a variety of optimization issues and timely reporting of an optimal alternatives. | When addressing an optimal solution, complex calculation is required. In small optima/minima zones, the searching process may get entrapped. | Many optimization issues, such as power management, may be solved with PSO. It may also be utilized for video graphical effects. |

Genetic algorithms (GA) [49] | Focused on population evolutionary computation, which use mutation, selection, and crossover to find the best solution. They do also have a fast convergence rate and can rapidly adapt to different types of optimization techniques, providing near-optimal outcomes in a fair amount of time. | While resolving, the requirements for the selection, mutation, and crossover processes must be satisfied. It also does not ensure that the best solution will be found. Similarly to PSO, the search process may become entrapped in localized optima/minima areas. | In natural sciences, such as architectures, genetic algorithms can be used to find a comprehensive solution. It is employed in image processing as well as learning the robot’s behavior. It is also utilized in distributed applications for data allocation. | |

Artificial Fish Swarm [50] | High precision, few variables, flexibility, and quick convergence are all advantages. It also adapts well to a variety of optimization situations, producing near-optimal approaches in a fair amount of time. | It has the same benefits as genetic algorithms, but it has drawbacks because to the lack of mutation and crossover. It is also no assurance that you will find the greatest answer. Furthermore, similarly to GA, the searching may become entrapped in specific optima/minima areas. | Fault tolerance, quick convergence speed, outstanding adaptability, and great precision are all advantages of artificial fish swarms. It frequently uses the general technique to tackle a variety of issues, including prey, followers, and swarms. Neural network learning, color quantization, and data segmentation are some of the other uses of AFS. | |

Artificial Intelligence Techniques | Artificial Neural Network [51] | Its evaluation time is quicker than prior algorithms, and it solves difficulties such as obtaining target objective functions for real-valued, binary, and other values. | It supports parallel processing and is hardware technology dependent. It provides unexpected answers but no indication of how they were achieved. | Handwriting recognition, picture compression, and stock exchange predictions all employ deep neural networks. |

Fuzzy Logic [52] | Fuzzy logic’s structure is simple to grasp, which makes it appealing to engineers who want to use it to operate machines. | It can be challenging to maintain precision while using fuzzy logic. | Fuzzy logic is widely utilized in spaceflight, the automobile industry, traffic control, and, most notably, in enhancing the transmission system’s performance. | |

SpecialTechniques | Manta Ray Optimization [53] | When compared to alternative optimizers, the computing cost is lower, and the results are more precise. | Its fine-tuning for finding solutions for optimization is ineffective, and its convergence rate is extremely slow, finding it less useful. | The manta ray approach is a bio-inspired optimizing algorithm inspired by the exceptional behavior of gigantic manta rays recognized for their rapidity. It is popular because of its high accuracy and low computational cost. |

Harris hawks Optimization [54] | It is well-known for its good performance, reasonable convergence, and high-quality optimization outputs. | It can be tough to grasp at times, and the computing complexity adds to the difficulty. | HHO is still in its early stages for academics, but it offers good convergence, precision, and speed for addressing real-world optimization issues. |

Ref | Objectives Functions | Details |
---|---|---|

[97] | $COE=\frac{{C}_{antot}}{{E}_{anserved}}$ | The objective function consists of COE that represents energy cost which is calculated as: total annualized cost (${C}_{antot}$)/total annual energy served (${E}_{anserved}$). The main problem is to calculate the energy cost and use optimization algorithms to solve it. It can also add some other costs like NPV (Net present Value) analysis. |

[98] | $F={\displaystyle {\displaystyle \sum}_{t=1}^{m}}\left({C}_{t}^{g}+C{r}_{t}^{g}+{C}_{t}^{ES-}-{C}_{t}^{l}-{C}_{t}^{ES+}+{\Omega}_{t}\right)\times \u2206t$ | It consists of ${C}_{t}^{g}$ that is the renewable energy cost and $C{r}_{t}^{g}$ is the non-renewable energy cost. ${C}_{t}^{ES-}$ is the cost of ESS charge and, $,{C}_{t}^{ES+}$ is the discharge cost of ESS. ${C}_{t}^{l},$ is the DR cost and ${\Omega}_{t}$ is the penalty of the energy not supplied. Its problem was to calculate the renewable energy cost. It lacks some resources, like PV, wind which costs can be added, if a microgrid enhance it by incorporating more resources and in this way, cost efficiency could be increased. |

[99] | $F=NPC+{\displaystyle {\displaystyle \sum}_{t=1}^{8760}}{P}_{b}\left(t\right)+{\displaystyle {\displaystyle \sum}_{t=1}^{8760}}{P}_{{H}_{2}}\left(t\right)+{\displaystyle {\displaystyle \sum}_{t=1}^{8760}}{P}_{w}\left(t\right)+{P}_{wt}+{P}_{{H}_{2}T}$ | The main objective function relies on NPC which is the net present cost for twenty operating years. ${P}_{wt},{P}_{{H}_{2,}}{P}_{{H}_{2}T}$ are the battery, water, water tank, hydrogen, and metal hydride tank penalty, represent. |

[100] | $F=C{F}_{t}^{OPR}+C{F}_{t}^{EMI}+C{F}_{t}^{RLB}$ | This objective function consists of $C{F}_{t}^{RLB},C{F}_{t}^{EMI},C{F}_{t}^{OPR}$ represent the emission, reliability and operation cost of microgrid. |

[101] | $F={C}_{in}^{MG}+{C}_{op}^{MG}\phantom{\rule{0ex}{0ex}}{C}_{op}^{MG}={\displaystyle {\displaystyle \sum}_{i=1}^{L}}\left({C}_{Fi}+{C}_{OMi}+{C}_{Si}+{C}_{Ei}\right)+{\displaystyle {\displaystyle \sum}_{j=1}^{M}}{C}_{OMj}^{ESS}-{C}_{G}^{MG}$ | This EMS cost composed of ${C}_{in}^{MG}$ is the investment cost and ${C}_{op}^{MG}$ is the operation cost. However, it can also add maintenance cost to further analyze the EMS cost. |

[102] | $F=Cos{t}^{Operating}+Cos{t}^{Emission}Cos{t}^{Operating}\phantom{\rule{0ex}{0ex}}Cos{t}^{Emission}={\displaystyle {\displaystyle \sum}_{t=1}^{T}}\left\{emissio{n}_{DG}\left(t\right)+emissio{n}_{s}\left(t\right)+emissio{n}_{Grid}\left(t\right)\right\}$ | The objective function of the microgrid is considered as an emission and operating cost. More cost can be added, if the microgrid involves PV, it will also make a system towards efficiency. |

[103] | $F={F}_{Cost}^{start-up}+{F}_{Cost}^{reserve}+{F}_{Cost}^{generation}+{F}_{Cost}^{DR}+{F}_{Emission}$ | The objective function of the microgrid is composed of emission functions and overall cost. It lacks investment cost and operational and maintenance cost, which is necessary for a system. |

[104] | $Frequenc{y}_{MG}={\displaystyle {\displaystyle \sum}_{s=1}^{Ns}}{\pi}_{s}\left({\displaystyle {\displaystyle \sum}_{h=1}^{Nh}}{\displaystyle \sum}_{l}\left|\Delta f\left(s,l,h\right)\right|\right)$ | It consists of $Frequenc{y}_{MG}$ that controls MG frequency as the EMS OF. |

[105] | $F={\omega}_{1}{\displaystyle {\displaystyle \sum}_{t=1}^{T}}\mathrm{cos}{t}^{t}+{\omega}_{2}{\displaystyle {\displaystyle \sum}_{t=1}^{T}}{Q}_{r,i}Emissio{n}^{t}$ | I is the price penalty factor while ${\omega}_{1}$ and ${\omega}_{2}$ are the non-negative coefficients for adjusting objective functions. |

[106] | $F={\displaystyle {\displaystyle \sum}_{t=1}^{T}}\left\{{\displaystyle {\displaystyle \sum}_{n=1}^{N}}\left({P}_{n,t}{B}_{n,t}+S{U}_{n}\times {y}_{n,t}+S{D}_{n}\times {z}_{n,t}+c{\pi}_{n,t}^{U}S{R}_{n,t}^{U}c{\pi}_{n,t}^{D}S{R}_{n,t}^{D}\right)+{\displaystyle {\displaystyle \sum}_{d=1}^{ND}}CD{R}_{d,t}+{\displaystyle {\displaystyle \sum}_{s=1}^{S}}P{r}_{t,s}S{C}_{t,s}\right\}$ | The cost function composed of, star-up costs, shut-down costs, and generation trade-off of DGs as well as security cost of the network and up and down reserves of demand response. However, if NPV and COE cost can be focused, it may take the system towards cost efficiency. |

[107] | $F={\displaystyle {\displaystyle \sum}_{t\u03f5T}}{C}_{t,money}+{\displaystyle {\displaystyle \sum}_{t\u03f5T}}{C}_{t,money}^{startup}-{\displaystyle {\displaystyle \sum}_{t\u03f5T}}{P}_{t,money}+{\displaystyle {\displaystyle \sum}_{t\u03f5T}}{\displaystyle {\displaystyle \sum}_{t\u03f5T}}{\mu}_{t,g}.{\pi}_{g}$ | It consists of ${C}_{t,money,}$ is the operation cost and ${C}_{t,money}^{Startup}$ represent the start-up costs while ${P}_{t,money}$ denote the total revenue. Last term denotes the penalty of the unmet load. Lastly, investment cost must be focused in a system which is also a necessary component. |

Ref | Microgrid Mode | Energy Source | Node System | |||
---|---|---|---|---|---|---|

Islanded | Grid-Connected | Type | Min Power | Max Power | ||

[99] | ✘ | ✔ | MT | 0 MW | 0.8 MW | IEEE 33 |

PV | 0 | 275 kW | ||||

[100] | ✔ | ✔ | WT. | 200 kW | 300 kW | IEEE 34- node systems |

PV | 80 kW | 120 kW | ||||

ESS | −20 kW | 200 kW | ||||

[137] | ✔ | ✔ | DiG | 100 kW | 790 kW | IEEE 33 bus system |

WT | 8000 kW | 45,000 kW | ||||

[138] | ✔ | ✔ | DiG | 1.60 MW | 1.80 MW | IEEE 33 bus system |

BES | 0 | 0.2 MW | ||||

[139] | ✔ | ✔ | PV | 0 | 11 MW | IEEE 84 bus system |

MT | 0 | 5 MW | ||||

ESS | 0 | 8 MW | ||||

[140] | ✘ | ✔ | DiG | 0.5 MW | 5 MW | IEEE 33 bus system |

MT | 0.1 MW | 2 MW | ||||

[141] | ✘ | ✔ | BES | 11.93 kW | 19.40 MW | IEEE 6 bus system |

DG | 200 kW | 300 kW | ||||

[142] | ✘ | ✔ | MT. | 0 kW | 1000 kW | IEEE 33 bus system |

WT | 0 kW | 1000 kW | ||||

PV. | 0 kW | 1500 kW | ||||

ESS | −1500 kW | 1500 kW | ||||

[143] | ✘ | ✔ | ─ | ─ | ─ | IEEE 30 bus system |

[144] | ✘ | ✔ | PV | 16.2 kW | 77.6 kW | IEEE 33-bus distribution network |

[145] | ✘ | ✔ | DiG | 10 kW | 100 kW | IEEE 33-bus test system |

ESS | 0 kW | 16.6 kW | ||||

EV | 0 kW | 111 kW | ||||

PV | 0 kW | 126.8 kW | ||||

[146] | ✘ | ✔ | ─ | ─ | ─ | IEEE 33 |

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Muqeet, H.A.; Javed, H.; Akhter, M.N.; Shahzad, M.; Munir, H.M.; Nadeem, M.U.; Bukhari, S.S.H.; Huba, M. Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges. *Sensors* **2022**, *22*, 2345.
https://doi.org/10.3390/s22062345

**AMA Style**

Muqeet HA, Javed H, Akhter MN, Shahzad M, Munir HM, Nadeem MU, Bukhari SSH, Huba M. Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges. *Sensors*. 2022; 22(6):2345.
https://doi.org/10.3390/s22062345

**Chicago/Turabian Style**

Muqeet, Hafiz Abdul, Haseeb Javed, Muhammad Naveed Akhter, Muhammad Shahzad, Hafiz Mudassir Munir, Muhammad Usama Nadeem, Syed Sabir Hussain Bukhari, and Mikulas Huba. 2022. "Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges" *Sensors* 22, no. 6: 2345.
https://doi.org/10.3390/s22062345