Abstract

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts.

Highlights

  • The results showed that this system accomplished demand response strategies, energy management scheduling, and maintaining the level of PV through V2G

  • The results showed that this combined energy management system was an optimal solution for the

  • A smart energy management system was suggested to optimize the scheduling process of onsite distributed generator (DG), energy storage system (ESS), and grid energy utilizing mixed-integer linear programming (MILP) with the consideration of the TOU-based demand response to enhance the consumption from RERs and to lessen operating electricity costs and the system load during the peak consumption hours

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Summary

Introduction

Power systems have been facing a lot of issues and challenges, including greenhouse gas (GHG) emissions, complicated network overloading, and rising consumption costs. The advancement in the microgrids provides an efficient solution for the intelligent monitoring of the system, with an automatic recovery system, persuasive demand control, and hightech controlling capabilities that are controlled with the help of efficient and intelligent sensors [3] It provides a variety of energy-saving and renewable energy integration opportunities for the microgrid for energy producers and consumers through the integrated energy management system (EMS). When local DGs and energy storages are inadequate in fulfilling the overall load demand, they can import electricity from the utility grid [6]. The involvement of these μGs in power systems lowers their operating energy costs, with the focus on the benefits of the distribution system [7]. The economic and environmental impact of solar PV with battery storage and electricity generation with different kinds of renewable energy resources were addressed

Recent
Proposed Conceptual Model
Objective Function
Load-Balancing Equality Constraint
ESS Constraints
Limitations of the Diesel Generator and Grid
Energy
Grid Energy Exchange
3.11. Solution Methodology
4.4.Results
Case Study
Different Seasons Case Study
11. The wind power speeds are often
Objective
Methods
Conclusions
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