Abstract

In this paper, an optimal strategy for greenhouse climate control is proposed. The objective is to minimize the total energy cost for greenhouse heating and cooling while keeping the greenhouse climatic conditions (temperature, relative humidity, carbon dioxide concentration) within required ranges. A dynamic model with three inputs and three outputs is adopted. The time-of-use electricity tariff is considered to calculate the energy cost. The proposed strategy is compared with an optimal control strategy which aims at minimizing the total energy consumption. In order to reduce the impact of system disturbance, a model predictive control (MPC) method is presented. The performance index (relative deviation mean) of the proposed MPC and an open loop control are calculated under 2 % system disturbances. The results show that compared with the strategy of minimizing energy consumption, the proposed strategy has higher energy consumption but lower cost. Moreover, MPC has better tracking performance than open loop control.

Abstract

Modelling and optimization of a large-scale urban energy system with sufficient spatial resolution is a complex challenge. By proper clustering technique, a large-scale problem could possibly be divided into small ones with high spatial resolution and accuracy. Existing literature tends to lower the complexity of large-scale urban energy system problem by accumulating demand profiles on the spatial dimension. This study proposed a new combined clustering approach which considers not only the spatial dimensions, but also the load characteristic of all buildings to solve a large-scale urban energy-water nexus optimization problem. The load complementarity can level off the total demand profile, which is helpful to obtain more economic benefit. The approach can divide district with a large number of buildings into small clusters including fewer buildings. By using complementarity indexes, the load heterogeneity of each cluster can be assessed. And the density of each cluster is used to investigate the distance among buildings within the same cluster. The combined clustering approach consists of two different routes: one is to lower down complementarity index with density as constraints; the other one is evaluating both two criteria simultaneously as a single objective. Through a case study, the proposed combined clustering approach can generate a new clustering map and finally save 4.4% total cost compared to density-based clustering approach.

Abstract

Cross Laminated Timber (CLT) is attracting worldwide attention, due to its durability, usability, and many other advantages. However, since CLT is made of wood, analysis of the hygrothermal performance is essential. In this study, the various conditions that affect the thermal moisture behavior were applied to the simulation for getting stable hygrothermal results. As a result, the standard of Passive house and Domestic wooden house, the climate condition, the presence of breathable water proofing paper, and the insulation alternatives of Expended Polystyrene (EPS) and Extruded Polystyrene (XPS) were applied. It was concluded that breathable waterproofing paper should be installed inside, and the applications of both XPS and EPS made no difference to moisture but the application of Mineral Wool was adversely affect to hygrothermal performance of the CLT wall system. The thickness of insulation should be designed according to the Passive house standard (0.15 W/m 2 K) rather than the Domestic (Korea) Wooden house standard (0.21 W/m 2 K).

Abstract

Modelling tools are frequently used to study China’s carbon policies while central and local governments initiate and implement carbon targets. Reviews of these low-carbon-planning tools and their usefulness, however, are not sufficient. In this regard, we review eight often used tools in China and suggest their applications and limitations. Tools are classified into four categories: computable general equilibrium, cost- optimization, benchmarking, and accounting tools. For China, application cases are recognized in addressing three research questions, i.e., emission scenario building, policy optimization, and carbon-policy impact analysis. From these studies, it is found that while tools usually require significant assumptions, the disclosure of them are in shortage and lack standardization and comparability. Tools also exclusively focus on policy- planning phase without attention to policy- implementation and policy-evaluation phases. Since the Chinese government has initiated three rounds of low- carbon-pilot-city programs, therefore, it is recommended that tool developers learn from some empirical evidence to integrate real policy outcomes into tools, e.g., policy effectiveness, expected implementation barriers, and required administrative power. Hence, analysts can complete a more holistic, evidence-based, and local-oriented policy suggestion. Standardization of model disclosure rules and evidence- based assumption-making are suggested to enhance comparability and mutual learning. Finally, modules to track progress in policy implementation and evaluation process can be added into tools for policy iteration and evidence collection.

Abstract

In this paper, we develop a residential grid-level optimization model, which incorporates both electrical consumption scheduling (ECS) systems and energy storage devices (ESDs), so as to lower the peak-to- average ratio (PAR) of electricity demand and reduce the costs of electricity supply and consumption. This model consists of three levels: household consumption optimization (solo opt), grid consumption optimization (base opt) and ESD allocation optimization (ESD opt). To evaluate this model, a realistic residential population of 180 households subdivided into subpopulations by household sizes and income levels was simulated using a bottom-up randomization approach, with electricity supply from conventional thermal generation (CTG). The results show that PAR can ideally be reduced to 1 with an optimal allocation of ESDs among households with positive bills savings.

Abstract

Cooling load and supply air parameters are the essential inputs of energy performance evaluation of the air condition system. Non-uniform air distribution (NUAD) benefits energy-efficient provision of comfortable and healthy indoor environment, but leads to increased complexities in the estimations of cooling load and supply air parameters. The non-uniformity-to- uniformity surrogation methodology enables the fully mixed air model to accurately estimate the cooling load and supply air parameters of NUAD, which is technologically convenient and computationally efficient. This study contributes to enriching the non- uniformity-to-uniformity surrogation methodology, which proposes a direct non-uniformity-to-uniformity surrogation by quantifying the ratios of NUAD to UAD regarding the cooling load as well as the supply air temperature/supply airflow rate for the constant-air- volume system/variable-air-volume system. The ratios to UAD are derived as the functions of the outside surface temperature of exterior wall, reference room air temperature and supply airflow rate/supply air temperature of UAD using data-driven modelling (Gaussian process regression). The proposed method is tested on an energy efficient NUAD, i.e., stratum ventilation. Compared with the conventional method which ignores the non-uniformity of stratum ventilation, the proposed method improves the estimation accuracy of the cooling load and supply air parameters by at least 89.1%.

Abstract

A novel systematic analysis framework based on modeling and simulation is provided for optimization of the methanation process using novel high-temperature tolerant catalysts. In this framework, the reactor is described by kinetic equation and energy balance. 3 schemes for methanation processes and 2 types of feedstock are investigated. In addition, different reaction temperature is considered for different schemes and different feedstock. Furthermore, the solution method is proposed for simulation and optimization the different process schemes. To compare the performance of different schemes, economic analysis including methane profit, steam profit, and compressing work consumption is investigated. Comprehensive considering different profit, the total profit for different schemes is provided and the optimal scheme is obtained.

Abstract

Green building can effectively reduce building energy consumption and pollutant emissions. China has spent great efforts on developing green commercial buildings(GCBs). In this paper, we use the ANP-SWOT method to evaluate the development of GCBs in China. First, the SWOT matrix (Strengths, Weaknesses, Opportunities and Threats) was used to analyze the influence factors of the GCBs industry and to formulate alternative strategies. Then, the Analytical Network Process (ANP) methodology was utilized to establish a network structure and to calculate the weights of factors, sub-factors and strategies. Finally, based on strategies ranking, we drew the policy implications. The results showed that the insufficient economic incentives and inadequate attention are main factors curbing the development of China’s GCBs and strengthening preferential policies and improving evaluation system are two more effective strategies of GCBs development in China.

Abstract

Urban form is a significant factor affecting building energy consumption and district energy efficiency design and its effects are difficult to quantify. This study aims to explore the effects of various urban forms on energy consumption at the community scale.
In this work, different urban forms for non-residential and residential districts were analyzed based on the generic form of buildings in Shanghai in terms of their overall energy consumption. Detailed simulations were carried out to quantitatively evaluate the impact of urban form on heating and cooling energy demand. The effect of morphological parameters including both building typology and urban morphology were examined using a dynamic building energy simulation tool, EnergyPlus.

Abstract

The battery model is the basis for battery state estimation, and its accuracy will directly affect the accuracy of the estimate. In the field of rail transit, combined with the actual operating conditions of the train to properly allocate the battery capacity, it is also necessary to establish an accurate battery model. At present, the commonly used battery model less considers the influence of temperature on the model parameters[1]. Because of the extreme low temperature conditions during the actual running of the train, this paper uses lithium iron phosphate battery as the research object, based on the second-order equivalent circuit model of the battery[2]. At the same time, considering the influence of low temperature on the available capacity of the battery[3], open circuit voltage, ohmic internal resistance and polarization parameters, a battery model suitable for room temperature below is proposed[4].