Intelligent demand side management for optimal energy scheduling of grid connected microgrids

Kumar, Ramesh; Raghav, L. Phani; Raju, D. Koteswara; Singh, Arvind R.

doi:10.1016/j.apenergy.2021.116435

Cited by 84 publications

(33 citation statements)

References 30 publications

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“…The probability density function ψ x, τ ð Þ j j 2 is evaluated to determine the probability of particles' appearing at the position x. The local attractor position is updated in every iteration as per Equations ( 26) and (27).…”

Section: Quantum Particle Swarm Optimizationmentioning

confidence: 99%

“…25,26 The prior research on the assimilation of utility-induced DSM tactics into the EMS problem is focused on load reduction 4 and strategic conversion. 5 In contrast to these works, the flexible load shaping strategy is introduced in recent research 27 to enhance utility and MG energy exchange costs. The overall operating cost of grid-connected MG is significantly reduced with DSM participation.…”

Section: Introductionmentioning

confidence: 99%

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Optimal day ahead energy consumption management in grid‐connected microgrids

Raghav

Kumar

Raju

et al. 2021

Intl J of Energy Research

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The day-ahead scheduling of microgrids in the presence of nondispatchable distributed generators (DGs) is a challenging task for microgrid operators. The valve point loading problem of distributed generators and its effect on inputoutput characteristics is not extensively covered in the published scientific literature on microgrids. In this research work, the day-ahead scheduling problem of microgrids is formulated in the presence of DGs with a nonconvex cost function. First, the flexible load-shaping based demand-side management strat-

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Section: Quantum Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal day ahead energy consumption management in grid‐connected microgrids

Raghav

Kumar

Raju

et al. 2021

Intl J of Energy Research

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“…The above state of the art summarizes various mathematical, 22 heuristic, 19 multi-agent-based, 14,15 and reinforcement learning-based 16 EMS strategies with a wide range of objectives. Furthermore, the quantum computational-based algorithms [28][29][30] are also adopted to resolve the problems of optimal scheduling in MG. In addition, applying various DR strategies unified with the EMS problem has enhanced operational costs.…”

Section: State Of the Artmentioning

confidence: 99%

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Singh

Ding

Raju

et al. 2021

Intl J of Energy Research

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Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

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“…Kumar et al [34] have suggested 3-level stochastic Energy Management Systems (EMS) for resolving the optimum day-ahead planning and reducing the cost of the microgrid connected system. The first level of the suggested approach was dataset generation and the second level was system configuration with specified constraints.…”

Section: Literature Reviewmentioning

confidence: 99%