scholarly journals Methods for multi-objective investment and operating optimization of complex energy systems

Energy ◽  
2012 ◽  
Vol 45 (1) ◽  
pp. 12-22 ◽  
Author(s):  
Samira Fazlollahi ◽  
Pierre Mandel ◽  
Gwenaelle Becker ◽  
Francois Maréchal
Keyword(s):  
Energy Systems ◽  
Complex Energy ◽  
Multi Objective

Developing a multi-objective multi-layer model for optimal design of residential complex energy systems

2022 ◽  
Vol 138 ◽  
pp. 107889
Author(s):  
Mehdi Davoudi ◽  
Mohammad Jooshaki ◽  
Moein Moeini-Aghtaie ◽  
Mohammad Hossein Barmayoon ◽  
Morteza Aien
Keyword(s):  
Optimal Design ◽  
Energy Systems ◽  
Complex Energy ◽  
Layer Model ◽  
Multi Objective

scholarly journals Eco-Emission Analysis of Multi-Carrier Microgrid Integrated with Compressed Air and Power-to-Gas Energy Storage Technologies

2021 ◽  
Vol 13 (9) ◽  
pp. 4681
Author(s):  
Khashayar Hamedi ◽  
Shahrbanoo Sadeghi ◽  
Saeed Esfandi ◽  
Mahdi Azimian ◽  
Hessam Golmohamadi
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
High Efficiency ◽  
Objective Assessment ◽  
Energy Systems ◽  
Vital Role ◽  
Compressed Air ◽  
Emission Analysis ◽  
Multi Objective ◽  
Power To Gas

Growing concerns about global greenhouse gas emissions have led power systems to utilize clean and highly efficient resources. In the meantime, renewable energy plays a vital role in energy prospects worldwide. However, the random nature of these resources has increased the demand for energy storage systems. On the other hand, due to the higher efficiency of multi-energy systems compared to single-energy systems, the development of such systems, which are based on different types of energy carriers, will be more attractive for the utilities. Thus, this paper represents a multi-objective assessment for the operation of a multi-carrier microgrid (MCMG) in the presence of high-efficiency technologies comprising compressed air energy storage (CAES) and power-to-gas (P2G) systems. The objective of the model is to minimize the operation cost and environmental pollution. CAES has a simple-cycle mode operation besides the charging and discharging modes to provide more flexibility in the system. Furthermore, the demand response program is employed in the model to mitigate the peaks. The proposed system participates in both electricity and gas markets to supply the energy requirements. The weighted sum approach and fuzzy-based decision-making are employed to compromise the optimum solutions for conflicting objective functions. The multi-objective model is examined on a sample system, and the results for different cases are discussed. The results show that coupling CAES and P2G systems mitigate the wind power curtailment and minimize the cost and pollution up to 14.2% and 9.6%, respectively.

Generation of Complex Energy Systems by Combination of Elementary Processes

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
A. Toffolo ◽  
S. Rech ◽  
A. Lazzaretto
Keyword(s):  
Design Optimization ◽  
Ad Hoc ◽  
Energy Systems ◽  
Optimization Procedure ◽  
Thermodynamic Cycle ◽  
Energy System ◽  
Design Parameters ◽  
System Configuration ◽  
Complex Energy ◽  
Synthesis Design

The fundamental challenge in the synthesis/design optimization of energy systems is the definition of system configuration and design parameters. The traditional way to operate is to follow the previous experience, starting from the existing design solutions. A more advanced strategy consists in the preliminary identification of a superstructure that should include all the possible solutions to the synthesis/design optimization problem and in the selection of the system configuration starting from this superstructure through a design parameter optimization. This top–down approach cannot guarantee that all possible configurations could be predicted in advance and that all the configurations derived from the superstructure are feasible. To solve the general problem of the synthesis/design of complex energy systems, a new bottom–up methodology has been recently proposed by the authors, based on the original idea that the fundamental nucleus in the construction of any energy system configuration is the elementary thermodynamic cycle, composed only by the compression, heat transfer with hot and cold sources and expansion processes. So, any configuration can be built by generating, according to a rigorous set of rules, all the combinations of the elementary thermodynamic cycles operated by different working fluids that can be identified within the system, and selecting the best resulting configuration through an optimization procedure. In this paper, the main concepts and features of the methodology are deeply investigated to show, through different applications, how an artificial intelligence can generate system configurations of various complexity using preset logical rules without any “ad hoc” expertise.

Complex Energy Networks Optimization: Part II — Software Application to a Case Study

2020 ◽  
Author(s):  
M. A. Ancona ◽  
M. Bianchi ◽  
L. Branchini ◽  
A. De Pascale ◽  
F. Melino ◽  
...  
Keyword(s):  
Energy Production ◽  
Internal Combustion Engines ◽  
Energy Systems ◽  
Distribution Networks ◽  
Optimal Scheduling ◽  
Small Scale ◽  
Complex Energy ◽  
Energy Networks ◽  
Energy Network

Abstract In order to increase the exploitation of the renewable energy sources, the diffusion of the distributed generation systems is grown, leading to an increase in the complexity of the electrical, thermal, cooling and fuel energy distribution networks. With the main purpose of improving the overall energy conversion efficiency and reducing the greenhouse gas emissions associated to fossil fuel based production systems, the design and the management of these complex energy grids play a key role. In this context, an in-house developed software, called COMBO, presented and validated in the Part I of this study, has been applied to a case study in order to define the optimal scheduling of each generation system connected to a complex energy network. The software is based on a non-heuristic technique which considers all the possible combination of solutions, elaborating the optimal scheduling for each energy system by minimizing an objective function based on the evaluation of the total energy production cost and energy systems environmental impact. In particular, the software COMBO is applied to a case study represented by an existing small-scale complex energy network, with the main objective of optimizing the energy production mix and the complex energy networks yearly operation depending on the energy demand of the users. The electrical, thermal and cooling needs of the users are satisfied with a centralized energy production, by means of internal combustion engines, natural gas boilers, heat pumps, compression and absorption chillers. The optimal energy systems operation evaluated by the software COMBO will be compared to a Reference Case, representative of the current energy systems set-up, in order to highlight the environmental and economic benefits achievable with the proposed strategy.

scholarly journals Multi-Objective Decision-Making for Hybrid Renewable Energy Systems for Cities: A Case Study of Xiongan New District in China

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6223
Author(s):  
Bin Ye ◽  
Minhua Zhou ◽  
Dan Yan ◽  
Yin Li
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Energy Systems ◽  
Energy System ◽  
Energy Utilization ◽  
Target Area ◽  
Renewable Energy Systems ◽  
Multi Objective ◽  
Hybrid Renewable Energy Systems ◽  
Hybrid Renewable Energy

The application of renewable energy has become increasingly widespread worldwide because of its advantages of resource abundance and environmental friendliness. However, the deployment of hybrid renewable energy systems (HRESs) varies greatly from city to city due to large differences in economic endurance, social acceptance and renewable energy endowment. Urban policymakers thus face great challenges in promoting local clean renewable energy utilization. To address these issues, this paper proposes a combined multi-objective optimization method, and the specific process of this method is described as follows. The Hybrid Optimization Model for electric energy was first used to examine five different scenarios of renewable energy systems. Then, the Technique for Order Preference by Similarity to an Ideal Solution was applied using eleven comprehensive indicators to determine the best option for the target area using three different weights. To verify the feasibility of this method, Xiongan New District (XND) was selected as an example to illustrate the process of selecting the optimal HRES. The empirical results of simulation tools and multi-objective decision-making show that the Photovoltaic-Diesel-Battery off-grid energy system (option III) and PV-Diesel-Hydrogen-Battery off-grid energy system (option V) are two highly feasible schemes for an HRES in XND. The cost of energy for these two options is 0.203 and 0.209 $/kWh, respectively, and the carbon dioxide emissions are 14,473 t/yr and 345 t/yr, respectively. Our results provide a reference for policymakers in deploying an HRES in the XND area.

scholarly journals HYRES: A Multi-Objective Optimization Tool for Proper Configuration of Renewable Hybrid Energy Systems

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 26 ◽  
Author(s):  
Katheryn Donado ◽  
Loraine Navarro ◽  
Christian G. Quintero M. ◽  
Mauricio Pardo
Keyword(s):  
Life Cycle Cost ◽  
Cost Benefit ◽  
Energy Systems ◽  
Energy System ◽  
Climatic Conditions ◽  
Hybrid Energy ◽  
Multi Objective ◽  
Viability Analysis ◽  
Hybrid Renewable Energy System ◽  
Cost Benefit Ratio

This paper presents the Hybrid Renewable Energy System (HYRES), a powerful tool to contribute to the viability analysis of energy systems involving renewable generators. HYRES considers various input parameters related to climatic conditions, statistical reliability, and economic views; in addition to offering multi-objective optimizations using Genetic Algorithms (GAs) that have a better cost-benefit ratio than mono-objective optimization, which is the technique used in several commercial systems like HOMER, a worldwide leader in microgrid modeling. The use of intelligent techniques in HYRES allows optimal sizing of hybrid renewable systems with wind and solar energy generators adapted to different conditions and case studies. The elements that affect the system design like buying and selling energy from/to the grid and the use of storage units can be included in system configuration according to the need. Optimization approaches are selectable and include Initial Cost, Life Cycle Cost, Loss of Power Probability, and Loss of Power Supply Probability.

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