A multi-objective optimization strategy for combined heat and power systems of the energy internet

Abstract Microgrids play a critical role in the transition from conventional centralized power systems to the smart distributed networks of the future. To achieve the greatest outputs from microgrids, a comprehensive multi-objective optimization plan is necessary. Among various conflicting planning objectives, emissions and cost are primary concerns in microgrid optimization. In this work, two novel procedures, i.e., non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), were developed to minimize emissions and cost in combined heat- and power-based (CHP) industrial microgrids (IMGs) simultaneously, by applying the most practical constraints and considering the variable loads. Two different scenarios, the presence and absence of photovoltaics (PV) and PV storage systems, were analyzed. The results concluded that when considering PVs and PV storage systems, the NSGA-II algorithm provides the most optimized solution in minimizing economic and environmental objectives.

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Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽

10.3390/en14154466 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4466

Author(s):

Maël Riou ◽

Florian Dupriez-Robin ◽

Dominique Grondin ◽

Christophe Le Loup ◽

Michel Benne ◽

...

Keyword(s):

Power Systems ◽

Rural Areas ◽

Energy System ◽

Design Stage ◽

Multi Objective Optimization ◽

Renewable Energy Resources ◽

Nsga Ii ◽

Renewable Integration ◽

Multi Objective ◽

Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

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A Multi-objective Optimization Strategy for Controlling the Structural Properties of Lightweight Mutiphasic PE/EVA Foams

Eco-friendly and Smart Polymer Systems ◽

10.1007/978-3-030-45085-4_144 ◽

2020 ◽

pp. 597-601

Author(s):

Sirwan Ghavami ◽

Mohammad-Hasan Khademi ◽

Farkhondeh Hemmati ◽

Ali Fazeli ◽

Jamshid Mohammadi-Roshandeh

Keyword(s):

Structural Properties ◽

Optimization Strategy ◽

Multi Objective Optimization ◽

Multi Objective

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Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽

10.3390/math10010133 ◽

2022 ◽

Vol 10 (1) ◽

pp. 133

Author(s):

Nien-Che Yang ◽

Danish Mehmood

Keyword(s):

Power Systems ◽

Optimization Problems ◽

Filter Design ◽

Manhattan Distance ◽

Multi Objective Optimization ◽

Optimal Method ◽

Pareto Solution ◽

Swarm Optimization ◽

Multi Objective ◽

Passive Power

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

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A Multi-Objective Optimization Strategy With Priority Ranking of the Design Objectives

16th Design Automation Conference: Volume 2 — Optimal Design and Mechanical Systems Analysis ◽

10.1115/detc1990-0071 ◽

1990 ◽

Author(s):

Masahide Matsumoto ◽

Jumpei Abe ◽

Masataka Yoshimura

Keyword(s):

Structural Design ◽

Torsional Rigidity ◽

Optimization Strategy ◽

Multi Objective Optimization ◽

Response Level ◽

Multi Objective ◽

Lower Priority ◽

Priority Ranking ◽

Priority Order ◽

Motorcycle Frame

Abstract Generally, two types of priorities are considered among multiple objectives in the design of machine structures. One of these objectives is named the “hard objective”, which is the absolutely indispensable design requirement. The other is called the “soft objective”, which has lower priority order. This paper proposes a multi-objective structural optimization strategy with priority ranking of those design objectives. Further, this strategy is demonstrated on the actual example of a motorcycle frame structural design which has three design objectives, (1) an increase in static torsional rigidity, (2) a reduction of dynamic response level, and (3) a decrease in the weight of the motorcycle frame.

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