Multi-dimensional signaling method for population-based metaheuristics: Solving the large-scale scheduling problem in smart grids

Soares, João; Ghazvini, Mohammad Ali Fotouhi; Silva, M.; Vale, Zita

doi:10.1016/j.swevo.2016.02.005

Cited by 41 publications

(46 citation statements)

References 53 publications

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“…In addition, constraints violations were easily avoided with signaling when compared with the standard approaches counterpart, which presented a high number of infeasibilities in both scenarios. This enable to conclude that, the use signaling leverages the better performance of the addressed methods in the ERM as it was demonstrated previously in single-objective problem [23]. In this case, the metaheuristic that featured better results was W-PSO, however, the execution time was higher, when compared with the NSGA-II and MOPSO, mainly in single-core.…”

Section: Discussionsupporting

confidence: 70%

“…In [23], a multi-dimensional signaling method is presented to improve metaheuristics performance to solve the energy scheduling problem. The method relies on a scheme to exchange signals between the metaheuristic core process, i.e., movement and evaluation stage.…”

Section: Multi-dimensional Signaling Methodsmentioning

confidence: 99%

“…The constraints for high-voltage (HV)/medium-voltage (MV) power transformers limits considering the power flow direction from HV to MV are taken into account, where the maximum amount of apparent power supplied from the upstream network cannot be higher than a predefined limit. This limit can correspond in reality to the capacity of the substation [23]. In the present ERM model the upstream network supply corresponds to the power bought from the external suppliers.…”

Section: Model Constraintsmentioning

confidence: 99%

“…The algorithm stops after the defined number of iterations is reached. This setup is widely used in other multi-objective metaheuristic-based algorithms [20,23,36,37].…”

Section: Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

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Enhanced Multi-Objective Energy Optimization by a Signaling Method

Soares¹,

Borges²,

Vale³

et al. 2016

Energies

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Abstract:In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO 2 ) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods' computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus). It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs), several distributed generation (DG), customers with demand response (DR) contracts and energy storage systems (ESS). The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.

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Section: Discussionsupporting

confidence: 70%

Section: Multi-dimensional Signaling Methodsmentioning

confidence: 99%

Section: Model Constraintsmentioning

confidence: 99%

“…The algorithm stops after the defined number of iterations is reached. This setup is widely used in other multi-objective metaheuristic-based algorithms [20,23,36,37].…”

Section: Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Multi-Objective Energy Optimization by a Signaling Method

Soares¹,

Borges²,

Vale³

et al. 2016

Energies

Self Cite

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“…In our opinion, this is a potential indicator that energy resource management-related research is getting more mature in the last years and ready for publication in journal. Energy resource management has been considered by some researchers as one of the most complicated optimization problems in power systems due to its combinatorial nature, nonlinearities, and large number of energy resources which leads to high dimensionality and highly constrained problems [23,[41][42][43]. Adequate optimization frameworks are a key part of the new paradigm brought by the smart grid operation.…”

Section: Complexitymentioning

confidence: 99%

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

et al. 2018

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This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

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