Distributed generation connected with AC, DC, or hybrid loads and energy storage systems is known as a microgrid. Campus microgrids are an important load type. A university campus microgrids, usually, contains distributed generation resources, energy storage, and electric vehicles. The main aim of the microgrid is to provide sustainable, economical energy, and a reliable system. The advanced energy management system (AEMS) provides a smooth energy flow to the microgrid. Over the last few years, many studies were carried out to review various aspects such as energy sustainability, demand response strategies, control systems, energy management systems with different types of optimization techniques that are used to optimize the microgrid system. In this paper, a comprehensive review of the energy management system of campus microgrids is presented. In this survey, the existing literature review of different objective functions, renewable energy resources and solution tools are also reviewed. Furthermore, the research directions and related issues to be considered in future microgrid scheduling studies are also presented.

/pdf/sustainable-solutions-for-advanced-energy-management-system-2ifl6r0d.pdf

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

To fulfill fast-growing energy needs, all energy sources should be utilized. Renewable energy is infinite and clean. However, its main disadvantage is that renewable energy sources are intermittent. A Hybrid Renewable Energy System (HRES) is built by integrating several distinct energy sources to deal with this problem. In regards to energy economy, economics, dependability, and flexibility, these hybrid systems can surpass the limits of individual energy producing technologies. The power capacity of HRESs increased from 700 GW to 3100 GW globally over the period 2000–2021. This study aimed to offer and analyze a comprehensive literature review of recently published works by several researchers in the area of HRESs. The HRES contains different Hybrid Energy Systems (HESs), which are categorized into three parts, namely, PV_Other, Wind_Other and PV_Wind_Other. These systems, based on different optimization techniques/software with techno-economic objective functions and constraints, are reviewed in this paper. The optimal sizing, control, and power management strategies of the HRES are elaborately discussed to harness its potential. It has been determined that Metaheuristic (MH) methods and HOMER software are mostly employed in the fields of HRES sizing, control, power management, and optimization. The review provides a critical analysis of the shortcomings of the existing HRES systems, while choosing optimization parameters, and control and power management schemes. Moreover, the study encapsulates the various challenges/barriers in adopting HRESs. Finally, this review highlights possible future opportunities for PV, Wind, and other HESs in the area of control, power management, optimization, and optimal sizing.

/pdf/scrutiny-of-hybrid-renewable-energy-system-for-control-power-19klw297.pdf

Scrutiny of Hybrid Renewable Energy System for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities

A nonlinear model-based dynamic optimal scheduling of a grid-connected integrated energy system

This article investigates a modelization of the energy management strategy of a grid‐tied photovoltaic (PV), wind generator, and battery system. The model uses a smart metering system to collect data from various electrical components of a microgrid. The power optimization strategy is modeled under demand response, including time‐of‐use pricing to create a home's energy flow more efficiently. Two optimal control strategies are developed to coordinate energy expenses, expressed as the cost of minimizing electricity supplied by the utility grid and maximizing the opportunity for energy to sell to the grid. A nonlinear open‐loop control and quadratic programming using model predictive control (MPC) are compared to assess the performance of the developed model. The design model takes into consideration the operational cost of batteries and degradation. The importance of the energy storage system is demonstrated in the context of energy conservation and gain on the demand side. MPC's capacity to specify the proper control and forecast future system reaction enables the proposed method to ensure significant daily energy and cost savings from 70.44 to 24.74 $ day−1 in the first scenario (without disturbances) and from 70.44 to 18.24 $ day−1 in the second case (with disturbances). In both methods, the net savings of greenhouse gas are approximately 288.10 (kgCO2‐eq) and 338.87 (kgCO2‐eq). These show better performance of the MPC compared to the linear model.

Toward an intelligent community microgrid energy management system based on optimal control schemes

The application of Integrated Energy Systems (IES) in establishing low-carbon, safe, and efficient energy supply systems has gained significant attention in recent years. However, as an energy stability link in IES, there is a lack of mature theoretical methods for energy allocation and optimal planning in the current multi-energy storage system (MESS) research. Hence, this paper proposes a method for configuring the capacity and selecting storage types in MESS within the IES. By considering the power response characteristics of different storage media, a combined ESMD-MPSO model is established that aims to enhance the economy and extend the service life of MESS. The model could allocate the power to the energy storage devices based on their power response capabilities and economic evaluation. Additionally, MESS application scenarios in both islanded and grid-connected IES are established. Highly adaptable energy storage devices are selected using the Analytic Hierarchy Process and the Fuzzy Comprehensive Evaluation method, resulting in four different multi-energy storage schemes for analysis. The results demonstrate that the method enables the determination of cost-optimal energy storage combination and capacity configuration for both scenarios. Furthermore, compared to existing methods, the approach achieves a 22.1 % and 9.6 % improvement in annual average costs for the two scenarios. This research could provide guidance for the planning and development of multi-energy storage systems within the IES. 

Optimal planning method of multi-energy storage systems based on the power response analysis in the integrated energy system

Modeling and optimization for distributed microgrid based on Modelica language

Two-level optimal scheduling method for a renewable microgrid considering charging performances of heat pump with thermal storages

Real-time junction temperature estimation model of photovoltaic modules for determining application scenarios

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Papers

A nonlinear model-based dynamic optimal scheduling of a grid-connected integrated energy system

Modeling and optimization for distributed microgrid based on Modelica language

Two-level optimal scheduling method for a renewable microgrid considering charging performances of heat pump with thermal storages

Real-time junction temperature estimation model of photovoltaic modules for determining application scenarios