Genetic algorithm-based operation strategy optimization and multi-criteria evaluation of distributed energy system for commercial buildings

Wen, Qingmei; Liu, Gang; Wu, Wei; Liao, Shengming

doi:10.1016/j.enconman.2020.113529

Cited by 32 publications

(7 citation statements)

References 30 publications

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“…The objective of the load demand optimization is to maximize the priority coefficient sum of the operating appliances f (S Ap ) to protect important critical loads in (25), where i is the appliance number. Cop i is the coefficient of priority of the i th appliance.…”

Section: B Load Demand Optimization Levelmentioning

confidence: 99%

“…A microgrid shall have the capability to be isolated. While conventional microgrids emphasize their role as a way to improve local energy independence and resilience, commercial building microgrids typically maintain power supply for at least enough time to cope with UG outages without damaging the system, and even in some cases, they may be able to power the system indefinitely [25], [26]. The microgrid topology of a commercial building, shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

Bai

Wang

Sun

et al. 2023

IEEE Open J. Power Energy

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This paper presents an online multi-level energy management system for local microgrids of commercial buildings that integrate roof-top photovoltaic sources, battery storage systems, utility grids, diesel generators, supercapacitors, and commercial buildings consisting of active orchestrated loads, to solve the uncertainty problem of sources and loads, while also optimizing the local microgrid operation cost of commercial buildings and the utilization rate of local renewable energy. The energy management system includes a long-term rolling optimization level, rule-based optimization level, and load demand optimization level. At the long-term rolling optimization level, an online rolling method of data restructuring is proposed, where measurement data, short-term prediction data, and day-ahead prediction data are reconstructed to reduce the uncertainty in photovoltaic source prediction and load demand prediction. Four methods are proposed for the energy management system and simulated in MATLAB/Simulink under three typical weather conditions, cloudy, sunny, and rainy. Simulation results show that the performance of Method 3 is closest to that of Method 4, whose data conditions are ideal; Method 3 reduces the operational cost of the commercial building microgrid and improves the utilization rate of photovoltaic sources, at the slight cost of non-critical load shedding.

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Section: B Load Demand Optimization Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

Bai

Wang

Sun

et al. 2023

IEEE Open J. Power Energy

View full text Add to dashboard Cite

show abstract

“…In BEMS, several things can be optimized [168]- [170]. Different works of literature have used different algorithms/methods to optimize energy management in the building [171]- [174] Most of the studies focus on either comfort factors or economic factors for the formulation of the objective function [175]- [179] Other factors include the minimization of the PAR in the load demand or the reduction of the production price [180].…”

Section: A Optimization Objectivesmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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“…It is a method to search for the optimal solution by simulating the natural evolution process. GA can be used for global optimization because it can extend the search space to the whole problem (Wen et al, 2020). Notably, the optimization procedure of the CCHP systems is shown in Figure 2.…”

Section: Optimizationmentioning

confidence: 99%

Potential Analysis and Optimization of Combined Cooling, Heating, and Power (CCHP) Systems for Eco-Campus Design Based on Comprehensive Performance Assessment

et al. 2021

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At present, countries all around the world have implemented energy-saving and emission reduction measures to achieve carbon neutralization. The combined cooling, heating, and power (CCHP) system is a high-efficiency energy system that can promote energy-saving and decrease carbon emissions. The choices of installed capacity and operation strategies impacts the economy, energy-saving, and environmental protection of combined cooling, heating, and power systems. The aim of this study was to determine the potential and comprehensive benefits of combined cooling, heating, and power systems based on the comprehensive performance assessment. In this study, a comprehensive performance index (CPI) method was presented to optimize the configurations and operation strategies of combined cooling, heating, and power systems in a Japan eco-campus, based on historically monitored data. According to the influencing factors of the combined cooling, heating, and power system in comprehensive performance index evaluation, the adaptability and development potential of economy, energy saving, and CO2 emission reduction were evaluated to consider future renovation of energy systems. In this study, we adopted a seasonal time-of-use (STOU) electricity price to evaluate the economic performance of combined cooling, heating, and power systems and compared them to the time-of-use (TOU) electricity price. The results indicated that the seasonal time-of-use electricity price was more economic in regions that had high cooling demand in summer. By comparing the CO2 emission coefficient of electricity in different regions in Japan, we could estimate that more than 40% of the cleaner energy ratio was positive. The results obtained can provide suggestions for the future development of combined cooling, heating, and power systems.

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Genetic algorithm-based operation strategy optimization and multi-criteria evaluation of distributed energy system for commercial buildings

Cited by 32 publications

References 30 publications

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

An Online Multi-Level Energy Management System for Commercial Building Microgrids With Multiple Generation and Storage Systems

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Potential Analysis and Optimization of Combined Cooling, Heating, and Power (CCHP) Systems for Eco-Campus Design Based on Comprehensive Performance Assessment

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