Multi-objective co-optimization of design and operation in an independent solar-based distributed energy system using genetic algorithm

The integration of renewable energy and flexible energy sources in buildings brings numerous benefits. However, the integration of new technologies has increased the complexity and despite the progress of optimization algorithms and technologies, new research challenges emerge. With the increasing availability of data and advanced modeling tools, stakeholders in the building sector are actively seeking a more comprehensive understanding of the implementation and potential benefits of energy optimization and an extensive up-to-date survey of optimization in the context of buildings and communities is missing in the literature. This study comprehensively reviews over 180 relevant publications on the management and optimization of energy flexibility resources in buildings. The primary objective was to examine and analyze prior research, with emphasis on the used methods, objectives, and scope. The method of content analysis was used to ensure a thorough examination of the existing literature on the subject. It was concluded that multi-objective optimization is crucial to enhance the utilization of flexible resources within individual buildings and communities. Moreover, the study successfully pinpointed key challenges and opportunities for future research, such as the need for accurate data, the complexity of the optimization process, and the potential trade-offs between different objectives.

Section: Heuristic and Meta-heuristic Methodsmentioning

confidence: 99%

A Review of Methodologies for Managing Energy Flexibility Resources in Buildings

Pedram,

Asadi,

Chenari

et al. 2023

“…Energies 2024, 17,1132 where S kS t is the k th class energy storage energy state at time t; Γ kS is the energy loss coefficient of the k th class energy storage equipment; η kS cha and η kS dis are the k th class energy storage equipment's charging and discharging efficiency; Γ kS min and Γ kS max are the minimum and maximum values of the proportion of the total capacity of the k th class energy storage equipment's energy state; P kS,cha t and P kS,dis t are the k th class energy storage equipment's charging and discharging power; M kS and M ks max are the k th class energy storage equipment's capacity and maximum power; B kS,cha t and B kS,dis t are the auxiliary binary variables; β ks is the ratio of the energy storage capacity to the energy storage equipment power.…”

Section: Energy Storage Constraintsmentioning

confidence: 99%

“…Multi-objective solving is a complex process, and commonly used methods include the ε-constraint method [15], the non-dominated sequential genetic algorithm-II (NSGA-II) [16,17], the multi-objective particle swarm algorithm (MOPSO) [18,19], and the multiobjective genetic algorithm (MOGA) [20]. However, these algorithms always have certain limitations; for example, the efficiency and completeness of the solution of NSGA-II will be affected by the size of the population, and MOPSO is computationally complex and has poor convergence.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Robust Optimization of Integrated Energy System with Hydrogen Energy Storage

Zhao,

Wei,

Zhang

et al. 2024

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province.

“…The study carried out by Bahlawan et al [16] proposed an integrated approach that combines the design, which relates to determining equipment capacities and operational variables that pertain to defining the optimal operation of a distributed energy system using surrogate modeling and dynamic programming, respectively. Huang et al [17] developed a method to optimize both the design and operational strategies of a distributed energy system powered by solar energy using a non-dominated sorting genetic algorithm (NSGA-II). Liu et al [18] proposed a novel dynamic operation strategy for DES to utilize the surplus power generated by PV in various ways to minimize the waste of generated power and ensure optimal operation based on real-time energy demand.…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Systems: Multi-Objective Design Optimization Based on Life Cycle Environmental and Economic Impacts

Maharjan,

Zhang,

Cho

et al. 2023

The distributed energy system (DES) represents an innovative approach to energy generation and distribution that promotes decentralization and diversification of energy sources. DESs can offer numerous benefits, including increased resiliency, reduced transmission losses, improved efficiency, and lower carbon emissions. The optimal design of a DES requires careful consideration of various factors such as geographical location, climate conditions, and energy demand patterns. This paper utilizes a multi-objective genetic algorithm to optimize the combination of technologies and their corresponding sizes in a distributed energy system for three types of commercial buildings—hospitals, large offices, and large hotels across eight different climate zones in the U.S. A range of technologies are considered for integration into the DES. These technologies include photovoltaic systems, wind turbines, combined heat and power systems, solar thermal collectors, and electrical and thermal energy storage. The two objectives considered are maximizing the reduction in carbon dioxide emissions and minimizing the life cycle costs for the DES. The purpose of this study is to optimize and evaluate the multi-objective design of distributed energy systems aimed at decentralizing and diversifying energy sources. The analysis of optimized DES designs across all 24 case scenarios shows that a balance between cost saving and emission reduction has been achieved. Although this study primarily focuses on specific buildings and climate zones, the methods and findings can be adapted for a wider variety of building types across different geographical locations, thus paving the way for more widespread adoption of optimized distributed energy systems.