• Energy Research

Abstract In developing countries with a large population and fast urbanization, High-rise Residential Buildings (HRBs) have unavoidably become a very common, if not the most, accommodation solution. The paradigm of HRB energy consumption is characterized by high-density energy consumption, severe peak effects and a limited site area for integrating renewable energy, which constitute a hindrance to the low-carbon transition. This review paper investigates low-carbon transition efforts in the HRB sector from the perspective of urban energy systems to get a holistic view of their approaches. The HRB sector plays a significant role in reducing carbon emission and improving the resilience of urban energy systems. Different approaches to an HRB low-carbon transition are investigated and a brief overview of potential solutions is offered from the perspectives of improving energy efficiency, self-sufficiency and system resilience. The trends of decarbonization, decentralization and digitalization in the HRB sector allow a better alignment with transitioning urban energy systems and create cross-sectoral integration opportunities for low-carbon transition. It is also found that policy tools are powerful driving forces in China for incentivizing transition behaviors among utilities, end users and developers. Based on a comprehensive policy review, the policy implications are given. The research is geared for the situation in China but could also be used as an example for other developing countries that have similar urbanization patterns. Future research should focus on quantitative analysis, life-cycle analysis and transdisciplinary planning approaches.

Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility potential greatly, but is usually oversimplified in the existing research. This paper aims to propose a novel method for simulating the dynamics of household energy-related activities and appliance usage, which provides realistic synthetic load profiles for smart energy management. To this aim, a highly resolved multi-agent system model is proposed, which comprises an Agent-Based Activity Chain Model (ABACM) and 34 types of common household appliance models. Firstly, the patterns of various occupant behaviors are obtained through mining of real-world residential time-of-use data. Then, the stochastic activity profiles of occupants are generated using the ABACM, with a root mean square error of 0.95%. Finally, the electricity consumption profiles of household appliances are simulated based on specific energy-related activities. The proposed method is validated and proved to be able to capture stochastic occupant behaviors and represent the dynamics of residential energy consumption. The future work will focus on using the proposed model for exploring the potential of residential demand response.

Abstract At present, China's economic and social development is restricted by many factors, such as environmental pollution and the supply of energy, land resources and water resources. Compared with traditional terrestrial photovoltaic (PV) systems, floating PV systems can save a lot of land and water resources and obtain higher power generation efficiency. Although the academics have reached a general consensus about the advantages of floating systems, very few in-depth studies focus on the specifications of floating PV systems. Therefore, this study first discusses the development of PV technology, then studies the power generation efficiency of floating PV systems, and finally comprehensively analyzes the advantages and potential of floating PV systems in China.

Abstract The prevailing High-rise Residential Buildings (HRBs) alleviate land shortage in modern cities, but energy sustainability in these energy-intensive constructions remains a big problem. The large available surface area of HRB makes rooftop attached PV technique a competent solution to utilize the local renewable energy. In the future, this can be expanded through facade installations. However, to which degree could PV generation support the HRB electricity consumption remains unclear. With more variations coming from renewable energy sources to the grid, peak-load electricity reduction will become critical. In this paper, the peak load shaving potential of rooftop attached PVs in HRB is estimated. Multi-agent system framework is employed to identify the building and occupant characteristics of HRB electricity consumption. A series of comparisons between demand and net demand profiles of HRB in a typical summer week are conducted. The results show that PV could effectively reduce the peak load during daytime, however, the maximum PV generation in the midday may cause new valleys in the net demand profiles. The results suggest that energy storage should be used to optimally match demand and supply in order to smoothen the net demand profiles.

Abstract This paper developed an agent-based trip chain model (ABTCM) to study the distribution of electric vehicles (EVs) charging demand and its dynamic characteristics, including flexibility and uncertainty, at different types of location. Key parameters affecting charging demand include charging strategies, i.e. uncontrolled charging (UC) and off-peak charging (OPC), and EV supply equipment, including three levels of charging equipment. The results indicate that the distributions of charging demand are similar as the travel patterns, featured by traffic flow at each location. A discrete peak effect was found in revealing the relation between traffic flow and charging demand, and it results in the smallest equivalent daily charging demand and peak load at public locations. EV charging and vehicle-to-grid (V2G) flexibility were examined by instantaneous adjustable power and accumulative adjustable amount of electricity. The EVs at home locations have the largest charging and V2G flexibility under the UC strategy, except for a period of regular working time. The V2G flexibility at work and public locations is generally larger than charging flexibility. Due to the fast charging application, the uncertainties of charging demand at public locations are the highest in all locations. In addition, the OPC strategy mitigates the uncertainty of charging demand.

Abstract Uncertainty is a common and critical problem for planning the capacity and operation of integrated energy systems (IESs). This study evaluates the effects of uncertainties on the capacity and operation of an IES. To this aim, system planning and operation with uncertainties are optimized by a two-stage stochastic programming model and compared with a referencing deterministic case. Specifically, the uncertainties of photovoltaic (PV) generation and energy demand are investigated. Regarding system capacity, a larger energy storage capacity is needed to accommodate a higher uncertainty. The superimposed uncertainties have a higher effect on system capacity than the sum of the effect of each uncertainty. The uncertainty of energy demand has a higher impact than the uncertainty of PV generation. Regarding system operation, the increase in operation cost is smaller than the increase in investment cost and total cost. In addition, the average flexibility provided by the energy storage increases with uncertainty and uncertainties affect the change rate for power charging/discharging of the electric energy storage. Regarding the effect on the grid, the uncertainties increase not only the magnitude of ramping-rate, but also the frequency of power-dispatch.

Abstract The idea that electric vehicles can connect to grid to serve as energy storage energy devices is compelling, especially in situations where traditional forms of storage, back-up energy supply is unavailable or expensive, or considering the volatility of frequency. So far, the viability and reliability of vehicle-to-grid(V2G) has become a very popular research topic. Previous studies mainly focused on the maximum potential of V2G capacity, which, however, is usually unrealistic to achieve. The potential capacity of V2G is relevant to the size of batteries and the penetration rates of electric vehicle. This article developed an agent-based model to estimate the real amount of electricity that vehicles can discharge to the grid. Three battery sizes under the penetration rate of 90% and the penetration rate from 10% to 90% were investigated. The results show that the increase of battery capacity led to an increase of V2G potential capacity from 1650.2 kW to 1868.5 kW and that increase from 10% to 90% in penetration rate could account for V2G potential capacity change from 162.8 kW to 1650.2 kW.