Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique

Mosa, Magdi A.; Ali, Ahmed

doi:10.1016/j.epsr.2020.106971

Cited by 59 publications

(26 citation statements)

References 28 publications

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“…Furthermore, it also considered the no-load costs of the distributed generators, non-linear losses within the DG, non-linear losses in a distribution system, and the start-up/shutdown costs of a DG. The researchers noted that the battery storage system played a vital role in minimising the generation costs and number of emissions, while also compensating for generation shortage [46].…”

Section: Energy Management Based On Mixed Integer Linear and Non-linear Programming Methodsmentioning

confidence: 99%

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

2021

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In the past few years, the application and research community has expressed a lot of interest in managing energy and power while using distributed generation systems. Electricity generation and its usage coordination are vital aspects of energy efficiency that can help in saving energy, decreasing energy costs, and fulfilling global emission objectives. Owing to the relevance of the topic, here, the researchers have presented a comparative and critical review of recent developments in the fields of energy management systems (EMSs) and power management systems (PMSs). Furthermore, the researchers also reviewed the various EMS and PMS methods that could be used for reviewing microgrid (MG) and nanogrid (NG) systems. The EMS for MG and NG systems helps in addressing important economic objectives like minimisation of operational costs after optimising the fuel costs, emission costs, and battery degradation costs, while also improving the life of the MG devices. Alternatively, the PMS helps in addressing technical objectives like improving the stability, flexibility, reliability, and quality of MG and NG systems. The researchers have also discussed the drawbacks and challenges affecting the widespread application of EMSs and PMSs.

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Section: Energy Management Based On Mixed Integer Linear and Non-linear Programming Methodsmentioning

confidence: 99%

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

2021

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“…14 Further, MGs increase the efficiency of renewable energy sources and help to implement net-zero buildings, campuses, and communities. 12 Depending on the power distribution system (DS), there are three types of MGs: alternating current MG (AC-MG), [15][16][17][18] direct current MG (DC-MG), [19][20][21][22] and hybrid microgrids (HMGs)-AC/DC. [23][24][25] The AC-MG features high penetration of distributed generation (DG) units and ESS [26][27][28] .…”

Section: Introductionmentioning

confidence: 99%

Efficiency and energy‐loss analysis for hybridAC/DCdistribution systems and microgrids: A review

Charadi

Chaibi

Redouane

et al. 2021

Int Trans Electr Energ Syst

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Historically, the development and design of distribution systems (DSs) and microgrids (MGs) were based primarily on alternating current (AC) as a traditional approach due to many advantages such as eliminating the need for synchronization, as well as the ease of integrating distributed energy resources (DERs). Recently, given the democratization of DER through local installations of renewable energy systems and appliances using power electronics, direct current microgrids (DC‐MGs) are gaining more and more momentum. In order to enable durable and economically viable use by integrating DC and AC DERs into microgrids, hybrid AC/DC microgrids (HMGs‐AC/DC) present one of the most promising approaches in eliminating the need for AC‐DC or DC‐AC conversions. The improvement of energy efficiency, protection, management, and control of this kind of systems are relevant research topics. This article provides an overview of theoretical works and industrial applications of hybrid AC/DC microgrids/distribution systems. In addition, an efficiency/energy‐losses study of different literature‐based works is discussed. Accordingly, a critical analysis is provided, and research perspectives related to this subject are outlined. This review article can be considered as a guide for future research on the efficiency and energy losses of hybrid AC/DC distribution systems/microgrids.

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“…Inspired by the classical economic dispatch of power systems, various studies have suggested different optimisation techniques to plan, schedule, and control the BESS in grid-connected microgrids. These studies have formulated the operation of the BESS as a dispatch optimisation problem and solved it using Linear Programming (LP) [2,3], Mixed Integer Programming (MIP) [4], Mixed-Integer Linear Programming (MILP) [5][6][7], and Mixed-Integer Nonlinear Programming (MINLP) [8,9]. In Chen et al [2], a general algebraic modelling system (GAMS) was developed and solved by CPLEX solver and then tested in a physical system based in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

“…These achievements are highly dependent on the proper estimation of spinning reserves, which is a big challenge while working on a real system. In Mosa and Ali [9], the MINLP algorithm was used to reduce the operational cost of a DC microgrid consisting of a photovoltaic (PV), fuel cell (FC), micro turbine (MT), diesel generator (DE), and battery/BESS. This study uses Egyptian grid load profiles over four seasons of the year based on the prediction.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation

et al. 2021

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Grid-connected microgrids consisting of renewable energy sources, battery storage, and load require an appropriate energy management system that controls the battery operation. Traditionally, the operation of the battery is optimised using 24 h of forecasted data of load demand and renewable energy sources (RES) generation using offline optimisation techniques, where the battery actions (charge/discharge/idle) are determined before the start of the day. Reinforcement Learning (RL) has recently been suggested as an alternative to these traditional techniques due to its ability to learn optimal policy online using real data. Two approaches of RL have been suggested in the literature viz. offline and online. In offline RL, the agent learns the optimum policy using predicted generation and load data. Once convergence is achieved, battery commands are dispatched in real time. This method is similar to traditional methods because it relies on forecasted data. In online RL, on the other hand, the agent learns the optimum policy by interacting with the system in real time using real data. This paper investigates the effectiveness of both the approaches. White Gaussian noise with different standard deviations was added to real data to create synthetic predicted data to validate the method. In the first approach, the predicted data were used by an offline RL algorithm. In the second approach, the online RL algorithm interacted with real streaming data in real time, and the agent was trained using real data. When the energy costs of the two approaches were compared, it was found that the online RL provides better results than the offline approach if the difference between real and predicted data is greater than 1.6%.

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Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique

Cited by 59 publications

References 28 publications

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications

Efficiency and energy‐loss analysis for hybridAC/DCdistribution systems and microgrids: A review

Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation

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