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The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme. [ABSTRACT FROM AUTHOR]

The regional energy system (RES) is a system that consumes multiple forms of energy in the region and achieves coordinated and efficient utilization of energy resources. The RES is composed of multiple micro energy systems (MESs); however, due to the mismatch of energy resources and different energy consumption within each MES, a large amount of clean energy is wasted, and each MES has to acquire extra energy. This significantly increases operation costs and contributes to environmental pollution. One of the promising ways to solve this problem is to deploy an energy storage system in the RES, which can make use of its advantages to transfer energy in space-time and fulfill the demand for loads in different periods, and conduct unified energy management for each MES in the RES. Nevertheless, a large number of users are deterred by the high investment in energy storage devices. A shared energy storage system (SESS) can allow multi-MESs to share one energy storage system, and meet the energy storage needs of different systems, to reduce the capital investment of energy storage systems and realize efficient consumption of clean energy. Taking multiple MESs as the object, this paper proposes a model and collaborative optimal strategy of energy management for the RES to accomplish high utilization of clean energy, environmental friendliness, and economy. First, the paper analyzes the internal energy supply characteristics of the RES and develops a model of the RES with an SESS. Then, the paper poses the management concept of load integration and unified energy distribution by using the operational information of each subsystem. An optimal operation strategy is established to minimize daily operation costs and achieve economic, environmentally friendly, and efficient operation of the RES. Third, by setting up scenarios such as no energy storage system and an independent energy storage system (IESS) of each MES and SESS, a case of a science and education park in Guangzhou, China, is illustrated for experiments. Numerical experiment results show that with an SESS built by the investor in the RES and applying the mentioned energy management strategy, the utilization of clean energy can be 100%, the operation costs can be reduced by up to 9.78%, the pollutant emission can be reduced by 3.92%, and the peak-to-valley difference can be decreased by 20.03%. Finally, the influence of energy storage service fees and electricity tariffs on daily operation costs is discussed, and the operation suggestions of the SESS are proposed. It validates the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Over the last 20 years, energy consumption in the residential sector in China has grown rapidly, and the growth is faster than that of any other energy form. To assess the limitations of the spatial characteristics of household energy consumption in urban areas, this paper selected Guangzhou as the research area. Specifically, the old town, core area, central area and peri-urban areas, which best reflect the evolutionary characteristics and spatial differentiation of households, were assessed. Based on the surveyed database of community-scale household energy consumption (N = 1097), the spatial heterogeneity of household energy consumption and carbon emissions at the community scale were assessed through exploratory spatial data analysis and the standard deviation ellipse method. The results report that (1) the main sources of energy consumption in Guangzhou households were water heating equipment, kitchen equipment and refrigeration equipment, which were related to the climatic conditions and cultural traditions of the city. (2) There was significant spatial heterogeneity in the carbon emissions from household domestic energy use in Guangzhou. (3) The economic level, the effects of the Lingnan culture and the characteristics of residents are the main drivers influencing the spatial characteristics of household energy consumption and carbon emissions in Guangzhou. We propose that policy development should actively promote energy-efficient equipment, such as water heating and cooling equipment, in urban households and take full account of the basic household energy needs of residents in old urban and suburban areas while promoting the development of low-carbon buildings. [ABSTRACT FROM AUTHOR]

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

Windows are responsible for the largest amount of energy loss related to building envelope. Past studies on the energy consumption of windows have mainly focused on residential and commercial buildings, while few studies have explored the energy consumption of windows in educational buildings. The sizes and locations of classroom windows affect not only the building energy consumption but also the indoor environment quality, which is an issue that needs to be balanced. This paper takes a classroom in Guangzhou as the object. Under the premise of meeting the lighting standards of the classroom, two parameters—window height and sill height —were simulated in different corridors of north–south and east–west classrooms to analyse the impact of their changes on the indoor environment as well as energy consumption based on the parametric platform Grasshopper and its plug-ins Ladybug and Honeybee. The energy consumption of a number of fenestration combinations on both sides of the classroom was optimized automatically by genetic algorithm, and the sizes and positions of windows that benefitted energy savings were obtained. In the best case ~16.6% of energy consumed in a whole year can be saved compared with the worst case. This conclusion can provide a quick decision-making process for the design of window shapes for the purpose of energy savings in the early design stage of educational buildings in the Pearl River Delta such that energy savings can be quickly and clearly guaranteed in the subsequent window configuration. [ABSTRACT FROM AUTHOR]

To achieve sustainable development, local governments are under enormous pressure to simultaneously achieve the three assessment tasks (TATs) of energy saving, CO₂ reduction, and pollution reduction. However, the TATs are often managed by different authorities and have three types of measures (TTMs) that correspond to them. The lack of adequate cooperation between these authorities has led to the inefficient investment of policy resources and even to policy conflicts, and the interactions among the TTMs are not yet known. To this end, this paper uses the MCEE model to assess the interactions among the TTMs quantitatively using Guangzhou, China, as a case study. The results showed the following. (1) According to the current development trend, if the authorities managing the TATs continue to work alone, they will not be able to fulfill the corresponding assessment tasks in the future. (2) The TTMs have interactions with each other, and their synergies are far greater than their trade-offs. From 2015 to 2035, it is expected that energy-saving measures can accomplish 54.39% of the CO₂ reduction tasks and 32.74% of the pollution reduction tasks indirectly, and low-carbon measures can accomplish 55.53% of the energy saving tasks and 27.20% of the pollution reduction tasks indirectly, However, environmental-protection measures will cause fewer trade-offs (energy demand and CO 2 emissions increase by 3.76% and 2.88%, respectively). (3) In some years, the contribution of interactions among the TTMs are even higher than their direct contribution to the TATs. Our findings suggest that intensive cooperation between authorities is necessary, and that the benefits of such cooperation will outweigh the disadvantages. • An integrated energy-economic-environmental assessment model for multiple indicators is established. • An index evaluation method for multiple synergies and trade-offs is established. • It reveals three types of measures do have interactions and their synergies are far greater than the trade-offs. • Intensive cooperation among authorities is necessary and the benefits of such cooperation will outweigh the disadvantages. [ABSTRACT FROM AUTHOR]

It is important for Chinese cities to advance sustainable energy consumption and ensure that they achieve their carbon emission peak and carbon neutrality. This paper develops a comprehensive assessment of the spatial variability and influencing factors of the quantity of household energy consumption per capita in mega-cities. Based on microlevel household survey data from Guangzhou, Guangdong Province, China (N = 1082), we analyze the spatial effects and factors of urban residents' energy consumption. A spatial econometric model with four spatial weight matrices is employed, and the nested spatial weight matrix, which holds practical economic and geographical significance, is set. We find that (1) the quantity of energy consumption per capita of all households in Guangzhou is 189.57 kgce/year and shows a spatial difference, with 177.5 kgce/year in the central urban area and 203.47 kgce/year in the outskirts urban area. In particular, households in the outskirts of urban areas present a higher percentage of "high" and "medium-high" energy consumption per capita than those in central urban areas. (2) The quantity of household energy consumption per capita of urban residents is characterized by significant spatial correlation and agglomeration, with "high-high" and "low-low" agglomeration in central urban areas. (3) The household labor force, the housing area and household income are the core factors that drive the quantity of household energy consumption per capita, while the region and educational level are also main factors. We observe the diversity of the energy transition by comparing the patterns of the quantity of household energy consumption per capita within the city. A comprehensive picture of the diversity underlying the energy transition applied across cases can be distilled. [ABSTRACT FROM AUTHOR]