Towards low-carbon cities: Patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-II

Liu, Hongjiang; Yan, Fang; Tian, Hua

doi:10.1016/j.ecolind.2021.108455

Cited by 33 publications

(15 citation statements)

References 64 publications

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“…45 Researchers have made efforts to further improve the comprehensive performance of NSGA-II for more complex multiobjective optimization problems. Liu et al 46 boosted the computation efficiency of NSGA-II by introducing parent inheritance from Kumar and Guria, 47 and they carried out selections by using the roulette selection algorithm from Da Silva et al 48 Liu et al 49 replaced the mutation of NSGA-II by the one from DE, which is beneficial for alleviating local convergence. Moreover, they divided all the individuals into several sequences, and the worst individual is deleted from each sequence in terms of crowding distance, which is helpful to the uniformity and stability of the individuals.…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

“…Moreover, they divided all the individuals into several sequences, and the worst individual is deleted from each sequence in terms of crowding distance, which is helpful to the uniformity and stability of the individuals. Owning to the advantages, the two improved NSGA-II methods in Liu et al 46,49 are considered for the biobjective optimization of DEED-PEV. In addition, we have proposed an NSGA-II variant to suit DEED-PEV, and it will be stated in Section 3.…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

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An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

Wu,

Zou,

Zhang

et al. 2024

Energy Science & Engineering

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This paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day.

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Section: Multiobjective Optimizationmentioning

confidence: 99%

Section: Multiobjective Optimizationmentioning

confidence: 99%

An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

Wu,

Zou,

Zhang

et al. 2024

Energy Science & Engineering

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“…Others explore the importance of policy innovation on low carbon development [80]. There are papers that propose novel approaches, such as the "multi-objective land use allocation optimisation model" [81] or that evaluate the performance of cities' low-carbon development [82]. However, while there are papers that investigate low-carbon transitions at the urban level, there is a lack of research on creating bottom-up approaches that consider the entire energy system of a city.…”

Section: Literature Reviewmentioning

confidence: 99%

City-Level Transition to Low-Carbon Economy

2022

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In recent years climate change has emerged as a global issue directly related to quality of life. In this context, one of the key goals in the next few decades will be to transition the global economy to a sustainable system. The nature of the energy planning process dictates the application of complex models. There is no universal solution to the energy planning problem. Each territory requires a bespoke strategy aimed at utilising its specific potential. The research presented in this paper explores reaching a zero-carbon energy system at the city level. It establishes a step-by-step decarbonisation method and proposes an energy transition index (ETI). The index presented is used to evaluate different renewable energy sources (RES) deployment scenarios in the context of affordability, self-reliance, and sustainability. The main aspects and barriers of deploying sustainable energy solutions are also explored. Some of the key challenges of RES deployment are identified as capital intensity, output variability, and the regulatory framework. The approach applied in the paper focuses on a city-level strategy in line with the goal of satisfying demand through local energy sources. The presented analysis offers two basic conclusions: (1) each territory requires a bespoke strategy that can optimally utilise its energy potential and (2) building a local zero-carbon system can be feasible only by implementing energy storage technologies.

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“…Meanwhile, responsiveness, digitization, artificial intelligence, carbon trading, smart grids, and electric vehicles are contributing to lowcarbon city [9]. Some studies have analyzed the construction methods of city's energy system, including multi-objective optimization [10] [11], sector coupling [12], and scenario-based method [13]. However, the development of energy prosumers (both energy consumers and energy producers, often using solar photovoltaic systems to generate electricity) are reshaping the traditional energy trading relationships and boundaries between energy production and consumption [14][15].…”

Section: Introductionmentioning

confidence: 99%

Building a Zero-carbon Energy System towards Carbon-neutral Cities and its Pilot Application in China

Chunyan¹,

Dai²,

Zhang³

et al. 2023

Preprint

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In the strategy of carbon peaking and carbon neutrality, cities and towns are the main battlefield while energy is the main force. Based on the synergy of multi-factors (source-network-load-storage) and multiobjectives (Lowest economic cost, lowest environmental cost, highest energy efficiency, highest electrification rate), built a zero-carbon energy system development planning model towards Carbon-neutral Cities.This paper presents the modeling approach of the zero-carbon energy system and presents its pilot application in Dazhangzhuang, one of the Smart Energy Town. To provide the reference for the construction of carbon-neutral cities energy system in China.

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Towards low-carbon cities: Patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-II

Cited by 33 publications

References 64 publications

An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

City-Level Transition to Low-Carbon Economy

Building a Zero-carbon Energy System towards Carbon-neutral Cities and its Pilot Application in China

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