Total Optimization of Energy Networks in a Smart City by Multi-Swarm Differential Evolutionary Particle Swarm Optimization

Sato, Masayuki; Fukuyama, Yoshikazu; Iizaka, Tatsuya; Matsui, Tetsuro

doi:10.1109/tste.2018.2882203

Cited by 30 publications

(19 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Natural gas and electric power utilities, and drinking water and wastewater treatment plant sectors are categorized into the energy supply-side group. The other sectors are categorized into the demand-side group [8,27].…”

Section: A Summary Of the Whole Smart City Modelmentioning

confidence: 99%

“…Namely, supplied energies by supply-side sectors and energy loads of demand-side sectors should be the same. The details are shown in [9,27].…”

Section: Sector Models In the Supply-side Groupmentioning

confidence: 99%

Section: Decision Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration

et al. 2019

Self Cite

View full text Add to dashboard Cite

This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters.

show abstract

Section: A Summary Of the Whole Smart City Modelmentioning

confidence: 99%

“…Namely, supplied energies by supply-side sectors and energy loads of demand-side sectors should be the same. The details are shown in [9,27].…”

Section: Sector Models In the Supply-side Groupmentioning

confidence: 99%

See 1 more Smart Citation

Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to the optimization model constructed in this paper, the IMOPSO algorithm was used to solve the calculation example. The population size of the IMOPSO algorithm was 80, and µ min = 0.5, µ max = 0.8, σ = 0.2 [30,31]. The installation position and capacity of the electric regenerative heating equipment are shown in Table 4.…”

Section: Results Analysis (1) Optimal Scheduling Results Of Etshe Consmentioning

confidence: 99%

“…The distribution network contained 19 distributed photovoltaics with a capacity of 0.5 MW, which were connected to nodes 7, 9, 14, 31, 35, 36, 52, 54, 59, 60, 63, 78, 85, 87, 95, 96, 113, 114, and 117. The distribution network also contained 10 distributed wind power generation with a capacity of 0.5 MW, which were connected to nodes 4,18,25,26,28,30,32,40,41, and 42. The rest of the data is consistent with the IEEE-33 system, which is not repeated here.…”

Section: Basic Datamentioning

confidence: 99%

Location and Capacity Selection Method for Electric Thermal Storage Heating Equipment Connected to Distribution Network Considering Load Characteristics and Power Quality Management

et al. 2020

View full text Add to dashboard Cite

High-permeability distributed wind power and photovoltaic systems are connected to the distribution network, which exacerbates the volatility and uncertainty of the distribution network. Furthermore, with the increasing demand of heating in winter and environmental protection, the wide use of electric thermal storage heating equipment (ETSHE) can promote distributed renewable energy utilization. However, an unplanned ETSHE connection to the distribution network may cause serious power quality problems. A new method of equipment location and capacity is proposed, which considered the improvement of power quality and load demand characteristics of the distribution network. First, based on heat load portrait technology, the node’s thermal load classification prediction was carried out to provide the data basis for the model solution. Second, the multi-objective optimal location and capacity programming model including harmonic distortion rate, voltage deviation, voltage fluctuation, and ETSHE cost was established. Then, the system nodes were preprocessed based on the sensitivity analysis method to reduce the number of installation nodes to be selected, and a feasible alternative set of installation nodes for the optimal configuration model of ETSHE could be obtained. Finally, the improved multi-objective particle swarm optimization algorithm was used to solve the model, and the data envelope analysis method was used to evaluate the power quality of each access scheme. The analysis of the numerical example shows that it can not only satisfy the user′s heat demand, but also effectively improve the power quality by rationally planning the location and capacity of ETSHE, which achieves the safe and efficient utilization of energy.

show abstract

Dynamic multi-swarm global particle swarm optimization

et al. 2020

View full text Add to dashboard Cite

Total Optimization of Energy Networks in a Smart City by Multi-Swarm Differential Evolutionary Particle Swarm Optimization

Cited by 30 publications

References 13 publications

Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration

Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration

Location and Capacity Selection Method for Electric Thermal Storage Heating Equipment Connected to Distribution Network Considering Load Characteristics and Power Quality Management

Dynamic multi-swarm global particle swarm optimization

Contact Info

Product

Resources

About