To predict the energy performance of a chilled water system more accurately, the hydraulic resistances of its water pipe network should be calibrated before simulation. However, it is a challenge to calibrate the hydraulic resistance of such a complex pipe network that are compose of chillers, terminal units, variable-speed pumps, valves and many pipes installed in different floors of a high-rise building. In this study, a new calibration method is proposed elaborately to adapt the vertical structure of the water pipe network in a high-rise building. The proposed calibration method utilized an optimization model and a general pipe network hydraulic solver. To overcome the severe nonlinear characteristic of the pipe network, Genetic Algorithm (GA) is used to solve the optimization model. Then, the proposed calibration method is validated in a real-life chilled water system in a high-rise building. With the hourly measured data from the chilled water system in operation in a typical summer day, the hydraulic resistances of 200 terminal units, 46 valves and 912 pipes are calibrated in detail. The calibrated hydraulic resistances are used to predict pressures and flow rates of the chilled water system in the next day. Compared with the uncalibrated simulation results, the average pressure error between the calibrated simulation results and measured data from the 42 onsite pressure meters is reduced from 2.2% to 0.6%. The average flow rate error between the calibrated simulation results and measured data from the 3 onsite flow rate meters is reduced from 5.3% to 0.9%.
Waste to energy is a promising way to ease the urban burden of waste treatment and hydrothermal carbonization (HC) can dewater the municipal wastes with high moisture efficiently with hydrochar left. The hydrochar with outstanding fuel characteristics can be used as fuel for incineration to generate power. To predict the fuel characteristics of hydrochar including the yield, higher heating value (HHV) and carbon content (C_char) based on the information of the wet municipal waste, machine learning methods have been explored in this work. Results show that the optimized Random Forest (80 trees with 10 maximum depths) has good multi-task prediction capability of fuel characteristics. The R 2 for the predictions of the yield, HHV and C_char are 0.80, 0.91 and 0.95, respectively. Moreover, according to the feature importance analysis, the yield of hydrochar is mainly determined by the temperature and water content of HC, while the HHV and C_char are dominated by the carbon and ash content of the feedstock, respectively.
Path anxiety is a major problem for electric vehicles, and charging infrastructure is indispensable to solve this problem. How to guide an electric vehicle to the optimal charging facility is a problem worth studying. This paper proposes a charging user discount rebate and reservation priority strategy for large scale electric vehicles (EV) access. First of all, the response characteristics of EV users to the discounts and reservation strategies of charging stations are analyzed and the user satisfaction decision model including economic satisfaction and reservation satisfaction is established. Secondly, the charging station benefit model with the goal of maximizing the benefits of the charging station is established. Eventually, the effectiveness of the proposed model is verified by a simulation considering two types of charging stations. The simulation is solved by using genetic algorithms (GA) and the simulation results show that the strategy can effectively attract users to charge and improve the interest of the charging station, as well as improve user satisfaction.
There are already numerous small-scale solar energy collectors on the roofs of buildings in many cities in China, which are used to provide domestic hot water in most circumstances. However, these separated small-scale solar energy collectors usually do not work sufficiently as expected, particularly for fear of pipe freezing crack in severe cold winter. On the other hand, these distributed small-scale solar energy collectors would have very convenient access to local district heating system. Hence, the buildings can consume the thermal energy from local district heating system and simultaneously produce heat to local district heating system when the solar energy collectors on their roofs are available. Therefore, the buildings can become solar heat prosumers to local district heating system. In this study, a configuration on solar heat prosumers is proposed to integrate with a general district heating system. Then a thermo-hydraulic model is developed to simulate the energy performance of distributed small-scale solar heat prosumers in district heating system. The proposed model is validated in a real life case study in a north Chinses city. The simulation results showed that the solar energy penetration was about 13% of the total heat consumption in heating season of 120 days.
The energy management strategy (EMS) plays an important role in the power system of hybrid-powered fuel cell vehicles in order to reduce hydrogen consumption and fuel cell performance degradation. This paper proposes a robust EMS based on the min-max game theory, where the EMS and the driver behavior are set as two virtual game players making decisions for opposite goals. First, a mathematical model of the hybrid-powered fuel cell vehicle is introduced, that include a transmission system, an ultracapacitor system, a fuel cell system, and the DC/DC converter. Then, a min- max game framework is constructed to describe the energy management problem of fuel cell/ultracapacitor hybrid-powered vehicle with uncertain environment. Finally, the high efficiency and robustness of the proposed strategy are validated by comparing it to the PID-based strategy in the dynamic driving condition.
Most of studies quantified the energy technology cost of integrated urban energy systems by calculating the levelized cost of energy (LCOE), but few analyze the contribution that an individual technology can bring to whole complex systems. This study introduces a generalized “system value” approach to quantify the contribution of each individual technology to the whole system as a function of the individual’s installed capacity. A generalized urban energy system optimal design model is formulated by Mixed Integer Linear Programming (MILP). An illustrative case study is conducted to explore the system values of different urban energy technologies. The results indicate that combined heating and power (CHP) presents the largest system value among all technologies. Heating/cooling supply technologies tend to provide lower system values compared to other electricity supply technologies due to the offset effect from adoption of energy saving strategies. Additionally, an individual technology’s system value varies with different penetration levels of that technology. Overall, this study presents a formulized method to assess the contribution of an individual technology from a systemic perspective, and aims to provide a new standpoint for decision-makers (instead of LCOE) for evaluating new technologies’ integrations to complex systems.
The integrated energy system is considered to beintroduced in buildings, which proposes a new effectiveapproach to improve energy structure in urban areas.The optimal design problem of building integratedenergy system is normally presented as mixed-integernonlinear programming model with deterministic anduncertainty parameters. Moreover, the uncertaintyproblem results in a more complex problem at a highcomputational cost. In present work, a two-stage multi-objective stochastic programming model underuncertainty is presented. The proposed model dependson clustering method to create different scenarios interms of solar radiation, wind speed and energy demand.In addition, the MINLP models of building integratedenergy system with stochastic scenarios anddeterministic scenarios is investigated to conduct trade-off Pareto optimization with cost-optimal andenvironment-optimal. The results indicate that thedeterministic programming model underestimates thecost and carbon emission of building integrated energysystem, while the result of stochastic programmingmodel is closer to the realistic design.
With significant impacts of climate change, the water amount and lake-marsh pattern has varied in the lake-marsh wetland system, which would damage the
ecological functions. Therefore, several regulation projects were implemented in the natural system, their advantages need to be proved. The Wolonghu Wetlands were applied as an example for exploring the effects of regulation projects on the carbon sequestration potential and corresponding ecological service value. The main results show that the water volume in the Wolonghu Wetlands has basically decreased during 1994 to 2013 as the natural runoff reduced, which further influenced the lake-marsh pattern as the wetland area
decreased. The carbon sequestration potential without and with regulation projects was 1,135,900.34 t/a and 1,341,823.80 t/a, respectively. The ecological service value of carbon sequestration potential without and with regulation projects was 8.75 billion yuan/a and 10.33 billion yuan/a, respectively. The carbon sequestration potential and corresponding ecological service value
with regulation projects was 18.13% and 18.6% greater than without regulation projects, respectively. It can be inferred that appropriate regulation projects could increase the carbon sequestration potential and corresponding ecological value of the lake-marsh wetland system, which was proved an efficiency approach facing to climate change on achieving the goal of low-carbon development.
This paper applies the real option approach (ROA) to analyze the economic viability of residential solar photovoltaic (PV) investment in the Philippines. From the point of view of a household (HH) owner, this approach evaluates the option values and optimal timing of investment to compare the attractiveness of investing in solar PV over continuing to use electricity from the grid. This further analyzes how various investment schemes and electricity prices uncertainty affect investment decisions. Results find that residential solar PV investment is profitable for all HH types investigated and that earlier investment in solar PV reduces the risk of opportunity loss from postponing the investment. Among the investment schemes analyzed, the distribution of solar PV cost in 5- or 10-year period shows to be the best investment strategy. The results are robust with various HH types investigated and with sensitivity in electricity prices.