With the increasing value of import food, the extra greenhouse gas (GHG) emission generated at food’s logistic phase can’t be ignored in China. While changing food consumers’ preference of import food over domestic food products has been difficult, the current COVID-19 pandemic may provide a bright side for shifting consumers’ intention. This paper first proposes that food safety, which used to be a highly anticipated attribute of import food, has lost its superiority due to possible contamination of the virus. Then the antecendent of such intention change has been analyzed. The preference changes for Chinese food consumers can then be guided for supply-side structural change to save energy. Further policy and managerial recommendations can be given based on the results for energy saving.
With the goal of carbon neutrality, low carbon transition is necessary in China. it is a common sense that there will be more RES(renewable energy sources) and thermal power will quit the main stage of power supply. RES will bring volatility to power system and threaten its reliability. Thermal power with flexibility modification and BESS(battery energy storage system) are two ways to ensure the safety of power system. This study provides a model on level of hour to calculate and analyze cost of power system in different penetration rates of RES. A province in China is taken as an example to compare flexible thermal power with BESS in five scenarios. It is found that thermal power with flexibility modification has an obvious advantage over BESS in short term while BESS is more potential in long term. An opinion for future of power system is given: thermal power still has a lot of things to do and it will quit by stepwise decreasing available hours instead of abandon of existing units in short term, BESS will play a more important role in long term.
A dual-stage day-ahead and real-time multi-energy cooperative optimization scheduling scheme is proposed for a grid-connected microgrid including photovoltaic d (PV), battery storage (BS), and gas turbine (GT). In the r day-ahead stage, the economic optimization is the aim. Based on the relative level of the tiered electricity prices, charge and discharge depreciation cost of BS, power generation cost of GT, day-ahead optimization strategy is proposed and the power allocations of PV, BS, GT, and GRID is formulated. Day-ahead panned interconnection power between the microgrid and main grid is passed to real-time scheduling stage as the constraint. In the real-time stage, the minimization of the operation costs at each dispatching period (15 mins) is taken as the objective. Real-time optimization strategy is proposed to modify the power allocations scheduled in the day-ahead stage. Results prove that the proposed dual-stage optimization of the scheduling has advantages in improving the operation economy of the microgrid.
Traditional energy planning is a one-way process from load forecasting to system optimisation, an approach that cannot support the increasing variability on both the supply and demand sides. This study proposes an energy simulation and system optimisation approach based on the state-space method for collaborative dynamic planning of the supply and demand sides of an integrated energy system. The case results show that taking into account the real-time dynamic characteristics of the load can improve the model accuracy; at the same time, system planning based on a synergistic supply and demand perspective can achieve overall optimality and rationalise the two-way interaction between the demand and supply sides.
Carbon pricing policy is one of the most efficient tools to mitigate carbon emission, while it alters the income distribution. The progressive individual income tax system redistributes income and reduces inequality. With a multi-regional dynamic CGE model, this study intends to explore the distributional effect of carbon pricing policy in China and to evaluate how the carbon revenue recycling scheme influences income inequality as well as the redistributive effect of individual income tax. Results show several key findings. First, in order to achieve the national emission peak by 2030, carbon pricing policy will lead to greater income inequality, increasing the after-tax Gini coefficient by 0.59% and 1.88% in 2030 and 2040 respectively. Second, if the carbon pricing revenue is recycled through the individual income tax return, the redistributive effects vary according to the design of the tax return rate. The proportional recycling scheme, i.e. all income groups have the same tax return rate, will continue widening the inequality, while the progressive recycling scheme, i.e. lower income groups have higher tax return rates, will narrow the income gap since 2030. Third, carbon pricing policy with a progressive recycling scheme influences income inequality by means of both reducing distortions of carbon policies on the economy and enhancing the redistributive effects of individual income tax. The carbon pricing policy increases the income inequality because of the domination of a positive economic distortion effect at first, while the carbon pricing policy turns to decrease the income inequality since 2025 because both distortion effect and redistributive effect are negative.
Integrated energy system (IES) takes advantage of flexibility based on energy synthetic utilization, thereby considered as the future energy carrier with great potential. The energy hub (EH) is essential for multi-level energy exploitation and flexible conversion between electricity and other energy sources. It is hoped that this research will contribute to a deeper understanding of the impact of energy coupling on system reliability and help to map out future organizations. This paper presents a method for measuring reliability in IES based on the thought of impact increment and hierarchical decoupling optimization. It follows a case-study design to verify the efficiency of the hybrid methodology given before. Supported by the numerical results, the effects of energy synthetic utilization in terms of system reliability are evaluated and also analyzed at a power flow level.
In order to achieve the carbon peak by 2030 and achieve the comprehensive goal of carbon neutrality by 2060, the research investigate the carbon emission performance of wastewater treatment plants (WWTPs) in China, and proposes suitable low-carbon strategies for WWTPs. The study utilized the operating data of 4346 WWTPs in China from 2014 to 2018, and calculated the carbon dioxide emissions and emission reductions. The carbon emissions include direct emissions (CH4 and N2O) during the treatment processes, and indirect carbon emissions embodied energy consumption. The carbon sinks comprise energy self-sufficient, energy saving by water reuse, and carbon emissions reduced by sludge composting. Research further simulated provincial carbon neutrality situation by meticulously giving each province a series of index determined by their per capita GDP, sunlight hours, per capita water sources, energy consumption of process design and agricultural land area. The results show that from 2014 to 2018, WWTPs in all provinces were still unable to achieve the target of carbon neutrality. Since the growth rate of carbon emissions is generally higher than that of carbon sequestration, the carbon neutrality efficiency has declined in the past five years. The direct carbon emissions of CH4 generally account for a relatively high proportion of all carbon emissions, and provinces with higher carbon neutrality level, such as Sichuan, Hubei and Shandong, also have a relatively low proportion of CH4 emissions. According to the simulating, we came up with the following results: nationwide, the net carbon emissions of WWTPs will reach its peak in 2027 and achieve carbon neutrality in 2052; provincially, the emissions of WWTPs will reach carbon neutrality in the time period between 2030-2060, and the timing of carbon neutrality is roughly the same as that of carbon peak; Except for Tibet with low carbon peak of WWTPs, carbon neutrality in other provinces can be achieved about 24 years after they reaching the peak of carbon emission respectively. Therefore, the study implicates more advanced emission reduction facilities of WWTPs are encouraged, such as the photovoltaic power generation devices, and suggest to increase the proportion of water reuse, and improve sludge utilization, in order to achieve the carbon neutrality goals.
As the most potential renewable energy, biofuel has become the fourth largest fuel source after coal, oil and natural gas. Promoting the extensive application of biofuel is an effective way to reduce carbon emission. At present, pipeline is one of the most efficient modes for transporting large amounts of liquid fuel over long distance. Transporting biofuel through the existing multiproduct pipelines is in line with the requirements of environmental protection, energy saving and low carbon economy. To determine the maximum transportation capacity of biofuel in a certain period is the premise of adopting multiproduct pipelines to transport biofuel. However, there are few studies on this issue. Considering the ordered pipeline capacity of clients for refined products, the limitations of existing pipeline equipment, and the transportation time limitation of biofuel, this paper develops a model for calculating the maximum transportation capacity of biofuel in pipelines. Finally, the applicability of the model is illustrated by applying it to a real-world pipeline in China. The results show that the model can reasonably calculate the maximum transportation capacity of biofuel in pipeline.
As Cornell is transitioning to a carbon-free energy system by 2035, the campus energy system of the future will be based on 100% renewable energy sources. Specifically, the electricity will be mainly sourced from the local electric grid, which is expected to be carbon-free in the next two decades. Earth source heat and lake source cooling will serve as the major source for base-load renewable heating and cooling, respectively. Multiple geothermal wells will be drilled to meet the base-load heating demand. A conventional chiller will continue to provide auxiliary cooling sources for hot summer days in addition to the lake source cooling system. Peak load will be fulfilled by introducing heat pumps, thermal energy storage, and green hydrogen. This study addresses the economically optimal future design by developing a multi-period optimization model, to provide insights for the campus energy systems transition.
In this work, we propose a novel multi-scale bottom-up optimization framework to address the decarbonization transition planning for power systems, which incorporates multiple types of information for each existing or new unit in the power systems, including its technology, capacity, and age. To reduce the computational challenge, a novel approach integrating Principal Component Analysis (PCA) with clustering techniques is proposed to obtain representative days. To illustrate the applicability of the proposed framework, a case study for New York State was presented. The proposed approach obtaining representative days using PCA coupled with K-means shows better performance than multiple state-of-the-art clustering approaches. The optimization results indicate that offshore wind, hydro, and utility solar are the main power sources in the state by the end of the planning horizon. To validate the optimization results, we conduct hourly power systems operations simulation for the entire planning horizon, and the result indicate that the error bar using the proposed framework is less than 1.5% in the case study.