Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

Tarraq, Ali; Elmariami, Faissal; Belfqih, Abdelaziz; Haidi, Touria

doi:10.1051/e3sconf/202123400086

Cited by 10 publications

(7 citation statements)

References 50 publications

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“…t∈T P in (t) ≤ Capacity • Lifetime cycle Lifetime calender (5) We perform a multi-objective optimization with two objectives, the total annual cost of the system and the annual CO 2 emissions, to compute a Pareto front. For this purpose, the energy system is first optimized with respect to costs only.…”

Section: Design Optimization Model For the Mathematical Optimizationmentioning

confidence: 99%

“…In the past energy systems and especially unit commitment problems were mostly modeled as Linear Programming (LP) or Mixed Integer Linear Programming (MILP) optimization models [2] and solved using mathematical optimization techniques such as the branch and cut algorithm. Today, various metaheuristics are widely used to solve unit commitment problems [3] and are even predicted to become the standard for design optimization problems in the near future [4,5]. While mathematical optimization is guaranteed to find the global optimum of a feasible problem, the complexity of global optimization problems can become so large that the global optimum cannot be found within a reasonable time.…”

Section: Introductionmentioning

confidence: 99%

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Metaheuristic Vs. Mathematical Optimization: A Comparison of Methods for the Design Optimization of Residential Building Energy Systems

Krisam,

Rosin,

Glombik

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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The energy system of residential buildings and their impact on the transition towards an emission-free energy supply has been a focus in a wide range of studies over recent years. For the design of energy systems, a variety of methods are used, most commonly heuristic, mathematical optimization and metaheuristic approaches. While the strengths and weaknesses of these methods are well known, knowledge about the discrepancy in results produced for the design of energy systems is limited. Moreover, metaheuristics have rarely been utilized in the field of household energy system planning. This leads to problems whenever findings from different studies are compared and raises the question about the optimal choice of methodology under given circumstances. To approach this question, we examine the energy system of a residential building with two different methods -a mathematical optimization and a metaheuristic optimization applied to the same MILP model. The energy system model considers a PV system, a heat pump, a heat and a battery storage system as well as a gas boiler. The layout and size of these components along with their operation are optimized. We compare the results regarding the difference in layout and size of individual components, investment costs, operational costs, CO 2 emissions and computational performance of the methods. In this case study, the mathematical optimization resulted in the best Pareto front. Using the metaheuristic approaches, it is possible to compute a Pareto front in a considerably shorter time. However, the quality of the Pareto front is significantly worse.

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Section: Design Optimization Model For the Mathematical Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metaheuristic Vs. Mathematical Optimization: A Comparison of Methods for the Design Optimization of Residential Building Energy Systems

Krisam,

Rosin,

Glombik

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

View full text Add to dashboard Cite

show abstract

“…All over the world, a major transition in the energy field has emerged due to the increase in CO2 emissions and the cost of electrical energy; renewable energy sources (RES) replace fossil fuel energy with a competitive cost and zero CO2 emissions [1]. This is the strategy of Morocco, located in North Africa, which has reviewed its development strategy much like other countries and changed its orientation by starting to invest more and more in renewable energy projects, given its geographical position and its significant potential in terms of solar and wind resources [2]- [4].…”

Section: Introduction − Motivation and Backgroundmentioning

confidence: 99%

An intelligent energy management system for optimum design and real-time operation

Zedak¹,

Belfqih²,

Boukherouaa³

et al. 2023

IJPEDS

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<span lang="EN-US">Planning and management of distribution networks has become a very difficult task, especially with the strong expansion of renewable energy sources (RES) which are intermittent in nature. Maintaining fluidity and reliability of real-time decisions while taking into consideration uncertainties related to production and increasing the profit of distribution network operators is the objective of the system proposed in this work. It is an intelligent energy management system dedicated to the management of grid-integrated RES and battery energy storage systems (BESS), composed of: i) a real-time control and data acquisition model, ii) a model for forecasting the intermittent parameters of RES based on neural networks, iii) a long-term planning model based on the optimal placement and size of RES and BESS, and iv) an hourly planning model for scheduling the energy distribution between energy sources. The non-dominated sorting genetic algorithm and the entropy-TOPSIS method (technique for order of preference by similarity to ideal solution) form the basic block of this model. To evaluate it, a modified IEEE 33 bus network was used for testing and the results, for short-term scheduling, proved that the system succeeds in maximizing profits and significantly minimizing CO<sub>2</sub> emissions, in addition to power losses and voltage drops.</span>

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“…For this reason, an overview of distribution network planning has been made. Metaheuristic optimization methods to better adapt renewable distributed generation to the electricity grid is anallyzed in detail [9]. A modified classification is mentioned for island detection methods by dividing them into single inverter, multi inverter, AC micro grid and DC micro grid [10].…”

Section: Introductionmentioning

confidence: 99%

A Short Review on the Optimization Methods Using for Distributed Generation Planning

2023

ijSmartGrid

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Industrial and social developments increase the need for energy. Due to the fact that fossil fuels will run out to meet energy needs, alternative technologies have been sought. Interest in renewable energy sources has increased. However, since renewable energy sources vary according to geographical conditions, their continuity must be ensured. Therefore, the focus has been on distributed generation systems. The components of distributed generation systems are divided into renewable, non-renewable and storage systems. Technical difficulties may arise in the connection of distributed generation systems with renewable resources. Therefore, it is very important to optimize distributed generation so that the distributed grid provides the expected power. Optimization systems are emphasized so as to decrease efficiency, reduce costs and cut down power fluctuations in distributed generation systems. In this study, distributed generation system, renewable energy source and energy storage system are mentioned. Metaheuristic methods for the efficiency of an energy system are studied. Evolution based algorithms can only keep the search space information in the iteration found, while swarm based algorithms can store the search space information throughout the iteration. Most metaheuristic algorithms use the biological cycle process, swarm behaviour, and physical laws. Evolution based and swarm-based optimization methods are examined in metaheuristic methods. As evolution based metaheuristic methods, genetic algorithm and swarm based optimization methods particle swarm optimization, ant colony optimization, grey wolf optimization, bat algorithm, whale optimization algorithm, cuckoo search algorithm are discussed. A general comparison of the investigated optimization methods is presented. It was concluded that the swarm optimization methods examined were able to make fast convergence and avoid local minima and computational efficiency evaluations.

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Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

Cited by 10 publications

References 50 publications

Metaheuristic Vs. Mathematical Optimization: A Comparison of Methods for the Design Optimization of Residential Building Energy Systems

Metaheuristic Vs. Mathematical Optimization: A Comparison of Methods for the Design Optimization of Residential Building Energy Systems

An intelligent energy management system for optimum design and real-time operation

A Short Review on the Optimization Methods Using for Distributed Generation Planning

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