Multi-objective optimization of smart community integrated energy considering the utility of decision makers based on the Lévy flight improved chicken swarm algorithm
“…Improvement upgrades: low-carbon and energy-saving renovation of communities such as green roofs and solar photovoltaic panels; overall environmental clean-up; improvement of barrier-free facilities; reasonable placement of public spaces in non-static wind areas and non-vortex areas according to the simulation results above; promotion and guidance of the spiritual and cultural aspects of people, etc.3. Innovative upgrades: additional public space for health emergencies in the context of the community environment; the establishment of a smart community system covering people, buildings, environment and other related information for real-time feedback for management and prevention control 42–44 ; improving age-friendly construction, etc.Figure 19.Overall environmental enhancement concept diagram.…”
Section: Discussionmentioning
confidence: 99%
“…3. Innovative upgrades: additional public space for health emergencies in the context of the community environment; the establishment of a smart community system covering people, buildings, environment and other related information for real-time feedback for management and prevention control [42][43][44] ; improving age-friendly construction, etc.…”
Section: Overall Community Environment Enhancementmentioning
Due to the global urbanization, the stock of older communities has been increasing drastically over time and living conditions have continued to deteriorate. Specifically, narrow building spaces and unreasonable community layouts would easily lead to the accumulation of air pollutants, which is more critical to public health as the recurrence of infectious diseases continues to be a threat to elderly residents. Thus, the need to improve and renovate the outdoor environment of old communities is urgent. This study proposes an environmental renovation strategy which explores the optimization of greenery and the application of wind deflector to improve the global and local wind environment. An old community in Suzhou, a national historical city in China, was selected as the case study. Based on field investigations, data measurement and comprehensive simulations, problematic areas were identified and renovation strategies were designed. As a result, the renovation achieved about 60% reduction of the vortex area and 40% reduction of the wind-free area, while also improving the green space ratio from 24% to 40.2%. The selection of greenery was also discussed regarding the antibacterial effect, carbon sequestration and air purification. A working flow chart has been synthesized for improving the outdoor environment of old communities.
“…Improvement upgrades: low-carbon and energy-saving renovation of communities such as green roofs and solar photovoltaic panels; overall environmental clean-up; improvement of barrier-free facilities; reasonable placement of public spaces in non-static wind areas and non-vortex areas according to the simulation results above; promotion and guidance of the spiritual and cultural aspects of people, etc.3. Innovative upgrades: additional public space for health emergencies in the context of the community environment; the establishment of a smart community system covering people, buildings, environment and other related information for real-time feedback for management and prevention control 42–44 ; improving age-friendly construction, etc.Figure 19.Overall environmental enhancement concept diagram.…”
Section: Discussionmentioning
confidence: 99%
“…3. Innovative upgrades: additional public space for health emergencies in the context of the community environment; the establishment of a smart community system covering people, buildings, environment and other related information for real-time feedback for management and prevention control [42][43][44] ; improving age-friendly construction, etc.…”
Section: Overall Community Environment Enhancementmentioning
Due to the global urbanization, the stock of older communities has been increasing drastically over time and living conditions have continued to deteriorate. Specifically, narrow building spaces and unreasonable community layouts would easily lead to the accumulation of air pollutants, which is more critical to public health as the recurrence of infectious diseases continues to be a threat to elderly residents. Thus, the need to improve and renovate the outdoor environment of old communities is urgent. This study proposes an environmental renovation strategy which explores the optimization of greenery and the application of wind deflector to improve the global and local wind environment. An old community in Suzhou, a national historical city in China, was selected as the case study. Based on field investigations, data measurement and comprehensive simulations, problematic areas were identified and renovation strategies were designed. As a result, the renovation achieved about 60% reduction of the vortex area and 40% reduction of the wind-free area, while also improving the green space ratio from 24% to 40.2%. The selection of greenery was also discussed regarding the antibacterial effect, carbon sequestration and air purification. A working flow chart has been synthesized for improving the outdoor environment of old communities.
“…This is because the weights are preset. Gao et al [14] proposed a multi-objective optimization method that effectively finds the optimal energy configuration scheme for intelligent communities using Levy flight and an improved chicken swarm algorithm, which enhances the algorithm's global and local search abilities. In addition, a comprehensive set of scenario simulations was conducted to test the model.…”
A dynamic reconfiguration method based on the improved multi-objective dung beetle optimizer (IMODBO) is proposed to reduce the operating cost of the distribution network with distributed generation (DG) and ensure the quality of the power supply, while also minimizing the number of switch operations during dynamic reconfiguration. First, a multi-objective model of distribution network dynamic reconfiguration with the optimization goal of minimizing active power loss and voltage deviation is established. Secondly, the K-means++ clustering algorithm is used to divide the daily load of the distribution network into periods. Finally, using the IMODBO algorithm, the distribution network is reconstructed into a single period. The IMODBO algorithm uses the chaotic tent map to initialize the population, which increases the ergodicity of the initial population and solves the problem of insufficient search space. The algorithm introduces an adaptive weight factor to solve the problem of the algorithm easily falling into a locally optimal solution in the early stage with weak searchability in the later stage. Levy flight is introduced in the perturbation strategy, and a variable spiral search strategy improves the search range and convergence accuracy of the dung beetle optimizer. Reconfiguration experiments on the proposed method were conducted using a standard distribution network system with distributed power generation. Multiple sets of comparative experiments were carried out on the IEEE 33-nodes and PG&E 69-nodes. The results demonstrated the effectiveness of the proposed method in addressing the multi-objective distribution network dynamic reconfiguration problem.
“…Mahdad (2019) uses the fractal search algorithm for reactive power optimization resource scheduling, but ignores the effect of DG on the system and the optimization algorithm can be further improved. To further improve the performance of intelligent optimization algorithms, a variety of intelligent algorithm improvement methods are widely used to deal with more complex optimization problems (Gao et al, 2021;Tseng et al, 2021). Aiming at the problem of low accuracy of traditional particle swarm optimization (PSO), Zou (2021) proposes fuzzy particle swarm optimization algorithm based on PSO to establish static and dynamic mathematical models of the system for achieving reactive power optimization.…”
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