Selecting a Meta-Heuristic Technique for Smart Micro-Grid Optimization Problem: A Comprehensive Analysis

Khan, Baseem; Singh, Pawan

doi:10.1109/access.2017.2728683

Cited by 147 publications

(51 citation statements)

References 88 publications

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“…In [29] six different meta-heuristic algorithms including TLBO are compared. These algorithms are, namely, Whale Optimization, Fire Fly, PSO, Differential Evaluation, and GA.…”

Section: Teaching-learing-based Optimization (Tlbo) Algorithmmentioning

confidence: 99%

“…Search agents of TLBO are found more effective to explore the feasible search space. It is reported in the [29] that TLBO search agents require the least effort. Among the above-mentioned six meta-heuristic techniques TLBO is reported as the best in most of the conditions.…”

Section: Teaching-learing-based Optimization (Tlbo) Algorithmmentioning

confidence: 99%

“…It need only the fewer parameters, thus reducing the burden of tuning the parameters. Thus, the TLBO algorithm is simple and effective [27][28][29]. Due to these advantages, TLBO has been adopted in this work.All works discussed here so far have tried to explore the technical benefit of DSM in design and operation areas.…”

mentioning

confidence: 99%

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Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Singh

Jha

2019

Energies

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This paper proposes Object-Oriented Usability Indices (OOUI) for multi-objective Demand Side Management (DSM). These indices quantify the achievements of multi-objective DSM in a power network. DSM can be considered as a method adopted by utilities to shed some load during peak load hours. Usually, there are service contracts, and the curtailments or dimming of load are automatically done by service providers based on contract provisions. This paper formulates three indices, namely peak power shaving, renewable energy integration, and an overall usability index. The first two indices indicate the amount of peak load shaving and integration of renewable energy, while the third one combines the impact of both indices and quantifies the overall benefit achieved through DSM. The application of the proposed indices is presented through simulation performed in a grid-tied microgrid environment for a multi-objective DSM formulation. The adopted microgrid structure consists of three units of diesel generators and two renewable energy sources. Simulation has been done using MATLAB software. Teaching-Learning-Based Optimization (TLBO) is adopted as the optimization tool due to its simplicity and independency of algorithm-specific control parameters. Five different cases of renewable energy availability with results validate the efficiency of the proposed approach. The results indicate the usefulness in determining the suitable condition regarding DSM application.Energies 2019, 12, 370 2 of 25 problems is the main area of research for system operators. DSM has appeared as a potential solution for many of these problems.Any scheme that involves alteration in operation of a complex system should be reliable to a certain level. A reliability study of a power system with DSM is presented in [1]. Since a plan for load curtailment can only be prepared when load forecasting is reliable, stochastic optimization and Gray Wolf Optimization (GWO)-based load forecasting and subsequent DSM programs are presented in [2,3]. Load shifting is also an alternative method of performing DSM. In this aspect, a corrective load shifting program is found to be a better option to conventional preventive load shifting [4,5]. In another work, loads are categorized in rigid and flexible types, and DSM is performed keeping in mind the adaptability of the flexible loads [6]. A novel application of DSM to smoothing the peaks of load curve in a small demand area in the Sultanate of Oman is described in [7]. The key contribution was load profile prediction based upon which the DSM program was prepared. The DSM program developed here handles the issue of variable power generation from solar energy-based generation. In a work, the uncertainty due to wind power generation and its impact on the DSM scheme has been discussed [8]. The methodology discusses the fulfillment of two key objectives of DSM, i.e., minimization of emission and cost. Battery storage and diesel-based energy sources are also employed with solar or wind generators to improve the reliability ...

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“…In [29] six different meta-heuristic algorithms including TLBO are compared. These algorithms are, namely, Whale Optimization, Fire Fly, PSO, Differential Evaluation, and GA.…”

Section: Teaching-learing-based Optimization (Tlbo) Algorithmmentioning

confidence: 99%

Section: Teaching-learing-based Optimization (Tlbo) Algorithmmentioning

confidence: 99%

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Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Singh

Jha

2019

Energies

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“…Thus, several heuristic methods to provide solutions to optimize the energy management problem have been presented in the literature. Authors in [38] present five optimization methods, including firefly (FF), particle swarm optimization (PSO), differential evolution (DE), GA, and teaching-learning-based optimization (TLBO) to address MG operation cost minimization problems. These algorithms were tested on different performance indicators with a standard benchmark.…”

Section: Energy Management Using Heuristic Optimization Approachesmentioning

confidence: 99%

“…Novel inverter [36,37] Minimize the burden on MG Energy exchange is not considered FF, PSO, DE, GA, TLBO [38] MG operation cost minimization. Energy exchange is not considered.…”

mentioning

confidence: 99%

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

et al. 2018

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This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.

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A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Alsalloum

Merghem-Boulahia

Rahim

2020

Int Trans Electr Energ Syst

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Summary The smart grid (SG) is an enhancement of the traditional electrical grid. Its main objectives are diverse, focusing mainly on minimizing the energy cost, as well as energy consumption. The smooth integration of renewable energy balances energy production and consumption. Dynamic pricing (DP) becomes one of the important integrated solutions, which comes in line with the global efficiency and reliability of smart grids. DP incentivizes the consumer to participate in the energy scheduling decision, mainly in the real‐time decision. Consumer represents the key challenge in the SG in perspective to make the energy system more efficient. Moreover, the success of smart grids relies mostly on the evolution of the widely used optimization methods. These include measures to encourage consumers and providers to be involved in the development of the SG. The main objective of this article is to review and analyze the recent research works regarding the pricing strategies and optimization methods utilized in the context of SG. A systematic analysis is thus performed by considering twofold views. First, it comprehensively approaches the pricing majors and visualizes the game theory used in real‐time pricing. Second, it describes the optimization methods used in the smart grid context and compares them in terms of their complexity.

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Selecting a Meta-Heuristic Technique for Smart Micro-Grid Optimization Problem: A Comprehensive Analysis

Cited by 147 publications

References 88 publications

Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

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