A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

Bukar, Abba Lawan; Tan, Chee Wei; Yiew, Lau Kwan; Ayop, Razman; Tan, Wen

doi:10.1016/j.enconman.2020.113161

Cited by 142 publications

(44 citation statements)

References 51 publications

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“…The system was composed of PV panels, battery systems, an electric vehicle (EV), and a relay board, which allowed or rejected the power fluxes according to certain rules aimed at minimizing the energy consumption from the public grid. Another similar work can be found in [73], in which an REMS with a nature-inspired grasshopper optimization algorithm (GOA) was presented. This optimization method was aimed at minimizing the cost of energy (COE) and the probability of the deficiency of energy supply.…”

Section: Ems Based On Metaheuristic Methodsmentioning

confidence: 99%

Towards Optimal Management in Microgrids: An Overview

et al. 2021

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A microgrid is a set of decentralized loads and electricity sources, mainly renewable. It can operate connected to and synchronized with a traditional wide-area synchronous grid, i.e., a macrogrid, but can also be disconnected to operate in “island mode” or “isolated mode”. When this microgrid is able to manage its own resources and loads through the use of smart meters, smart appliances, control systems, and the like, it is referred to as a smart grid. Therefore, the management and the distribution of the energy inside the microgrid is an important issue, especially when operating in isolated mode. This work presents an overview of the different solutions that have been tested during the last few years to manage microgrids. The review shows the variety of mature and tested solutions for managing microgrids with different configurations and under several approaches.

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Section: Ems Based On Metaheuristic Methodsmentioning

confidence: 99%

Towards Optimal Management in Microgrids: An Overview

et al. 2021

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“…The scope of this work is oriented towards tertiary control. Various research works dedicated to tertiary control are reported in [7,18,19].…”

Section: The Hes Architecture and Modeling In Stateflowmentioning

confidence: 99%

Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding

et al. 2021

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This study investigates the potential application of Stateflow (SF) to design an energy management strategy (EMS) for a renewable-based hybrid energy system (HES). The SF is an extended finite state machine; it provides a platform to design, model, and execute complex event-driven systems using an interactive graphical environment. The HES comprises photovoltaics (PV), energy storage units (ESU) and a diesel generator (Gen), integrated with the power grid that experiences a regular load shedding condition (scheduled power outages). The EMS optimizes the energy production and utilization during both modes of HES operation, i.e., grid-connected mode and the islanded mode. For islanded operation mode, a resilient power delivery is ensured when the system is subjected to intermittent renewable supply and grid vulnerability. The contributions of this paper are twofold: first is to propose an integrated framework of HES to address the problem of load shedding, and second is to design and implement a resilient EMS in the SF environment. The validation of the proposed EMS demonstrates its feasibility to serve the load for various operating scenarios. The latter include operations under seasonal variation, abnormal weather conditions, and different load shedding patterns. The simulation results reveal that the proposed EMS not only ensures uninterrupted power supply during load shedding but also reduces grid burden by maximizing the use of PV energy. In addition, the SF-based adopted methodology is envisaged to be a useful alternative to the popular design method using the conventional software tools, particularly for event-driven systems.

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“…The component G i from the centered equation can be computed as;

G_{i} = - g \vec{e_{g}},

where g is the acceleration constant of gravity and

\vec{e_{g}}

is a unit vector, directing towards the center of the earth. Figure 17 depicts the strength of social force by varying the values of intensity of attraction and the attractive length scale 94 …”

Section: Meta‐heuristic Optimization Algorithmsmentioning

confidence: 99%

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

Arfeen

Sheikh

Azam

et al. 2020

Concurrency and Computation

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Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.

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A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

Cited by 142 publications

References 51 publications

Towards Optimal Management in Microgrids: An Overview

Towards Optimal Management in Microgrids: An Overview

Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

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