Application of large underground seasonal thermal energy storage in district heating system: A model-based energy performance assessment of a pilot system in Chifeng, China

Seasonal thermal energy storage (STES) technology is a proven solution to resolve the seasonal discrepancy between heating energy generation from renewables and building heating demands. This research focuses on the performance assessment of district heating (DH) systems powered by low-grade energy sources with large-scale, high temperature underground STES technology. A pilot DH system, located in Chifeng, China that integrates a 0.5 million m3 borehole thermal energy storage system, an on-site solar thermal plant and excess heat from a copper plant is presented. The research in this paper adopts a model-based approach using Modelica to analyze the energy performance of the STES for two district heating system configurations. Several performance indicators such as the extraction heat, the injection heat and the storage coefficient are selected to assess the STES system performance. Results show that a lower STES discharge temperature leads to a better energy performance. A sensitivity analysis of the site properties illustrates that the thermal conductivity of soil is the most influential parameter on the STES system performance. The long-term performance of the STES is also discussed and a shorter stabilization time between one and two years could be achieved by discharging the STES at a lower temperature.This research is part of the seasonal storage for solar and industrial waste heat utilization for urban district heating project funded by the Joint Scientific Thematic Research Programme (JSTP)–Smart Energy in Smart Cities. We gratefully acknowledge the financial support from the Netherlands Organisation for Scientific Research (NWO). We would also like to thank our research partners from Tsinghua University working on the project of the International S&T Cooperation Programof China (ISTCP) (project No. 2015DFG62410). Without their efforts, we would not have been able to obtain the technical data to conduct the case study

Multi-criteria decision making under uncertainty in building performance assessment

Building performance assessment is complex, as it has to respond to multiple criteria. Objectives originating from the demands that are put on energy consumption, acoustical performance, thermal occupant comfort, indoor air quality and many other issues must all be reconciled. An assessment requires the use of predictive models that involve numerous design and physical parameters as their inputs. Since these input parameters, as well as the models that operate on them, are not precisely known, it is imprudent to assume deterministic values for them. A more realistic approach is to introduce ranges of uncertainty in the parameters themselves, or in their derivation, from underlying approximations. In so doing, it is recognized that the outcome of a performance assessment is influenced by many sources of uncertainty. As a consequence of this approach the design process is informed by assessment outcomes that produce probability distributions of a target measure instead of its deterministic value. In practice this may lead to a “well informed” analysis but not necessarily to a straightforward, cost effective and efficient design process. This paper discusses how design decision making can be based on uncertainty assessments. A case study is described focusing on a discrete decision that involves a choice between two HVAC system designs. Analytical hierarchy process (AHP) including uncertainty information is used to arrive at a rational decision. In this approach, key performance indicators such as energy efficiency, thermal comfort and others are ranked according to their importance and preferences. This process enables a clear group consensus based choice of one of the two options. The research presents a viable means of collaboratively ranking complex design options based on stakeholder’s preferences and considering the uncertainty involved in the designs. In so doing it provides important feedback to the design team

Impacts of surface model generation approaches on raytracing-based solar potential estimation in urban areas

Raytracing-based methods are widely used for quantifying irradiation on building surfaces. Urban 3D surface models are necessary input for raytracing simulations, which can be generated from open-source point cloud data with the help of surface reconstruction algorithms. In research and engineering practice, various algorithms are being used for this purpose; each leading to different mesh topologies and corresponding performance. This paper compares the impacts of four different reconstruction algorithms by investigating their performance using DAYSIM raytracing simulations. The analysis is carried out for five configurations with various urban morphologies. Results show that the reconstructed models consistently underestimate the shading influence due to geometrical shrinkages that emerge from the various model generation procedures. The explicit algorithms, with Generic Delaunay a notable example, have better performance with less embedded error than the implicit algorithms in both daily and annual simulations. Results also show that diffuse irradiance is responsible for larger contributions to the overall error than direct components. This effect becomes more prominent when modeling reflected irradiation in urban environments. Additionally, the work shows that solar elevation and shading geometry types also affect the error magnitude. The paper concludes by providing reconstruction algorithm selection criteria for photovoltaic practitioners and urban energy planners

A design optimization method for solar-driven thermochemical storage systems based on building performance simulation

The challenge of the temporal mismatch between energy supply and demand in buildings is growing with the increasing share of renewable energy in total energy consumption. Among all the state-of-the-art energy storage solutions, thermochemical heat storage shows a unique potential thanks to its considerable energy density, acceptable cost, and negligible heat loss. For this reason, it becomes a promising alternative to common sensible heat storage solutions for building applications. The integration of such a novel technology in buildings necessitates a method for the assessment of its potential impact and benefit, the comparison to common alternatives, and the optimization of the system design. This work proposes a method based on modeling and simulation of the interaction between the thermochemical heat storage system and the building using a data-driven surrogate model of the storage system in combination with a building performance simulation engine. The data-driven model was developed and validated based on laboratory measurements of a novel closed-loop thermochemical heat storage system, the heat battery (HB). The method was demonstrated in a case study to identify the optimal size of the HB in a solar-driven configuration based on a residential building use case. The results show that the heat battery can digest the thermal energy transferred from the solar thermal collector to reduce the original electricity consumption for heating the detached house (0.7 MWh to 1.0 MWh in considered cases) without any obvious sacrifice in thermal comfort and that the small-scale HB (with a storage volume below 160 liters) shows efficient usage of the designed storage capacity

Optimizing the total energy consumption and CO₂ emissions by distributing computational workload among worldwide dispersed data centers

Major internet service providers have built and are currently building the world's largest data centres (DCs), which has already resulted in significant global energy consumption. Energy saving measures, from chip to building level, have been introduced gradually in recent decades. However, there is further potential for savings by assessing the performance of different DCs on a wider scale and evaluating information technology (IT) workload distribution strategies among these DCs. This paper proposes a methodology to optimize the electricity consumption and CO2 emissions by distributing IT workload across multiple imaginary DCs. The DCs are modelled and controlled in a virtual test environment based on a building energy simulation (BES) tool (TRNSYS). A controller tool (Matlab) is used to support testing and tuning of the optimization algorithm. A case study, consisting of the distribution of IT workload across four different types of data centers in multiple locations with different climate conditions, is presented. The case study will illustrate.</p

Optimizing the total energy consumption and CO₂ emissions by distributing computational workload among worldwide dispersed data centers

Major internet service providers have built and are currently building the world's largest data centres (DCs), which has already resulted in significant global energy consumption. Energy saving measures, from chip to building level, have been introduced gradually in recent decades. However, there is further potential for savings by assessing the performance of different DCs on a wider scale and evaluating information technology (IT) workload distribution strategies among these DCs. This paper proposes a methodology to optimize the electricity consumption and CO2 emissions by distributing IT workload across multiple imaginary DCs. The DCs are modelled and controlled in a virtual test environment based on a building energy simulation (BES) tool (TRNSYS). A controller tool (Matlab) is used to support testing and tuning of the optimization algorithm. A case study, consisting of the distribution of IT workload across four different types of data centers in multiple locations with different climate conditions, is presented. The case study will illustrate.</p