Implications of temporally and geographically realized energy use for electrified transportation

2014 Spring.Plug in electric vehicles (PEVs) are vehicles that use energy from the electric grid to provide tractive and accessory power to the vehicle. The nonexistent (electric vehicles) or reduced-sized (plug in hybrid vehicles) engine in these vehicles results in high energy conversion efficiencies, lower GHG emissions, and reduced environmental pollution. Consumer demand for these vehicles is limited by their reduced range relative to conventional vehicles. Range limitations in PEVs are primarily due to the lower onboard energy storage capacity of lithium ion (720kJ/kg) relative to gasoline (47.2MJ/kg), and the range sensitivity of PEVs to accessory loads, primarily cabin conditioning loads, is higher. The factors such as local ambient temperature, local solar radiation, length of the trip and thermal soak have been identified to affect the cabin conditioning power requirements and to therefore affect vehicle range. The steady increase in consumer demand for PEVs has resulted in research initiatives by USDOE, the automotive industry and utility industry to overcome these range limitations. The focus of this research is to develop a detailed systems-level approach to connect HVAC technologies and usage conditions to social, environmental, and consumer-centric metrics of performance. This is accomplished through the development of a toolset that consider transient environmental parameters, real world driver behavior, charging behavior, and regional passenger fleet population for HVAC system operation. The resulting engineering toolset can be used to determine geographical distribution of energy consumption by HVAC systems in electric vehicles, identify regions of US where EVs can elicit positive user response, evaluate the sensitivity of PEV range to the local weather conditions, identify times of use to extract maximum performance from PEVs, establish HVAC component specifications, and optimize vehicle energy management strategies and technologies. A case study with the alternative accessory technology such as a combination of phase change materials to provide for heating and cooling is explored. The results of this research show that PEV HVAC energy consumption is geographically and temporally disparate, that range variability may be more of a driver of consumer dissatisfaction than actual range, and that HVAC energy management and technologies can reduce the variability in PEV range and may thereby improve PEV consumer acceptability

MODEL PREDICTIVE CONTROL OF ENERGY SYSTEMS FOR HEAT AND POWER APPLICATIONS

Building and transportation sectors together account for two-thirds of the total energy consumption in the US. There is a need to make these energy systems (i.e., buildings and vehicles) more energy efficient. One way to make grid-connected buildings more energy efficient is to integrate the heating, ventilation and air conditioning (HVAC) system of the building with a micro-scale concentrated solar power (MicroCSP) sys- tem. Additionally, one way to make vehicles driven by internal combustion engine (ICE) more energy efficient is by integrating the ICE with a waste heat recovery (WHR) system. But, both the resulting energy systems need a smart supervisory controller, such as a model predictive controller (MPC), to optimally satisfy the en- ergy demand. Consequently, this dissertation centers on development of models and design of MPCs to optimally control the combined (i) building HVAC system and the MicroCSP system, and (ii) ICE system and the WHR system. In this PhD dissertation, MPCs are designed based on the (i) First Law of Thermo- dynamics (FLT), and (ii) Second Law of Thermodynamics (SLT) for each of the two energy systems. Maximizing the FLT efficiency of an energy system will minimise energy consumption of the system. MPC designed based on FLT efficiency are de- noted as energy based MPC (EMPC). Furthermore, maximizing the SLT efficiency of the energy system will maximise the available energy for a given energy input and a given surroundings. MPC designed based on SLT efficiency are denoted as exergy based MPC (XMPC). Optimal EMPC and XMPC are designed and applied to the combined building HVAC and MicroCSP system. In order to evaluate the designed EMPC and XMPC, a com- mon rule based controller (RBC) was designed and applied to the combined building HVAC and MicroCSP system. The results show that the building energy consump- tion reduces by 38% when EMPC is applied to the combined MicroCSP and building HVAC system instead of using the RBC. XMPC applied to the combined MicroCSP and building HVAC system reduces the building energy consumption by 45%, com- pared to when RBC is applied. Optimal EMPC and XMPC are designed and applied to the combined ICE and WHR system. The results show that the fuel consumption of the ICE reduces by 4% when WHR system is added to the ICE and when RBC is applied to both ICE and WHR systems. EMPC applied to the combined ICE and WHR system reduces the fuel consumption of the ICE by 6.2%, compared to when RBC is applied to ICE without WHR system. XMPC applied to the combined ICE and WHR system reduces the fuel consumption of the ICE by 7.2%, compared to when RBC is applied to ICE without WHR system

Integrated heat air and moisture modeling and simulation

An overall objective of our work is to improve building and systems performances in terms of durability, comfort and economics. In order to predict, improve and meet a certain set of performance requirements related to the indoor climate of buildings, the associated energy demand, the heating, venting and air conditioning systems and the durability of the building and its interior, simulation tools are indispensable. In the field of heat, air and moisture transport in building and systems, much progress on the modeling and simulation tools has been established. However, the use of these tools in an integrated building simulation environment is still limited. Also a lot of modeling work has been done for energy related building systems, such as solar systems, heat pump systems and heat storage systems. Often, these models focus on the systems and not on the coupled problem of building and systems. This thesis presents the development and evaluation of an integrated heat, air and moisture simulation environment for modeling and simulating dynamic heat, air and moisture processes in buildings and systems. All models are implemented in the computational software package MatLab with the use of SimuLink and Comsol. The main advantages of this approach are: First, the simulation environment is promising in solving both time and spatial related multi-scale problems. Second, the simulation environment facilitates flexible linking of models. Third, the environment is transparent, so the implementation of models is relatively easy. It offers a way to further improve the usage and exchange of already developed models of involved parties. More than 25 different heat, air and moisture related models are included in this work. Most of the models are successfully verified (by analytical solutions or by comparison with other simulation results) and/or validated (by experimental data). The use of the simulation environment regarding design problems is demonstrated with case studies. Overall is concluded that the simulation environment is capable of solving a large range of integrated heat, air and moisture problems. Furthermore, it is promising in solving current modeling problems caused by either the difference in time constants between heating venting and air conditioning components and the building response or problems caused by the lack of building simulation tools that include 2D and 3D detail simulation capabilities. The case studies presented in this thesis show that the simulation environment can be a very useful tool for solving performance-based design problems

Review of Cabin Thermal Management for Electrified Passenger Vehicles

Peer reviewe

Simulation of a model-based optimal controller for heating systems under realistic hypothesis

An optimal controller for auxiliary heating of passive solar buildings and commercial buildings with high internal gains is tested in simulation. Some of the most restrictive simplifications that were used in previous studies of that controller (Kummert et al., 2001) are lifted: the controller is applied to a multizone building, and a detailed model is used for the HVAC system. The model-based control algorithm is not modified. It is based on a simplified internal model

Resilient cooling of buildings: state of the art review

Name of the research project : IEA Annex 80 – Resilient Cooling of Buildings Publisher: Institute of Building Research & Innovation ZT GmbH, AustriaThis report summarizes an assessment of current State-of-the Art resilient cooling strategies and technologies. It is a result of a collaborative work conducted by participants members of IEA EBC Annex 80. This report consists of four chapters. In the first chapter are included relevant technologies and strategies that contribute to reducing heat loads to people and indoor environments. These technologies/strategies include Advanced window/glazing and shading technologies, Cool envelope materials, Evaporative Envelope Surfaces, Ventilated Envelope Surfaces and Heat Storage and Release. In the second chapter are assessed cooling strategies and technologies that are responsible for removing sensible heat in indoor environments: Ventilative cooling, Evaporative Cooling, Compression refrigeration, Desiccant cooling system, Ground source cooling, Night sky radiative cooling and High-temperature cooling systems. In the third chapter various typologies of cooling strategies and technologies are assessed inside the framework of enhancing personal comfort apart from space cooling. This group of strategies/technologies comprise of: Vertical-axis ceiling fans and horizontal-axis wall fans (such fixed fans differ from pure PCS in that they may be operated under imposed central control or under group or individual control), Small desktop-scale fans or stand fans, Furnitureintegrated fan jets, Devices combining fans with misting/evaporative cooling, Cooled chairs, with convective/conductive cooled heat absorbing surfaces, Cooled desktop surfaces, Workstation micro-air-conditioning units, some including phase change material storage, Radiantly cooled panels (these are currently less for PCS than for room heat load extraction), Conductive wearables, Fan-ventilated clothing ensembles, Variable clothing insulation: flexible dress codes and variable porosity fabrics. In the fourth chapter technologies and strategies pertinent to removing latent heat from indoor environments are assessed. This group includes Desiccant dehumidification, Refrigeration dehumidification, Ventilation dehumidification, and Thermos-electric dehumidification.Preprin

강화학습을 적용한 실용적인 건물 시스템 제어

학위논문(석사) -- 서울대학교대학원 : 공과대학 건축학과, 2021.8. 조성권.HVAC 및 조명과 같은 기존 시스템과 간헐적 재생 에너지, 에너지 저장 시스템 등과 같은 새로운 시스템에도 대응해야 하므로 현대 건물 시스템 제어는 복잡해지고 있습니다. 이에 따라, 건물 시스템 제어기는 건물의 동적 거동에 스스로 적응할 수 있어야 하고 다목적 최적화 결과를 반영할 수 있어야 한다. 강화학습 (reinforcement learning, RL)을 사용하여 전술된 건물 제어기의 성능을 달성할 수 있다는 것은 널리 알려져 있지만, RL을 실제 건물에 적용하기 위해서는 해결해야 할 과제들이 있다: (1) RL의 초기 훈련 기간 동안 불안정한 제어는 예상치 못한 비용을 야기할 수 있다. (2) 여전히 대부분의 RL 기반 제어 전략은 일상적 실무에 적용하기에는 시설 관리자 입장에서 이해하기 어렵고 제어 전략에 대한 해석을 수행할 수 없다. RL 알고리즘을 건물 제어에 적용한다는 것은 의사결정의 주체가 인공지능이 된다는 것을 의미한다. 이때, 건물의 소유주와 운영자는 인공지능 기반 건물 제어기의 의도 및 의사결정 과정에 대한 해석 및 이해를 할 필요가 있다. 첫 번째 과제를 해결하기 위해, RL 에이전트를 사전 학습하고 이를 위해 새로운 개념의 시뮬레이션 모델인 연합 모델이 제안된다. 연합 모델은 빌딩 시스템을 물리적 인과 관계에 따라 모듈로 나누고 각 모듈을 데이터 기반 모델로 개발하여 빌딩 시스템에 대한 시뮬레이션을 수행하는 통합 데이터 기반 모델이다. 대상 건물의 냉방 시스템 시뮬레이션 모델은 6개의 모듈로 구성되고 각 모듈은 BEMS에서 수집된 데이터를 사용하여 개발된다. 연합 모델은 제1법칙 기반 시뮬레이션 모델의 한계 (예: 위상 규칙, 모델 보정)를 극복할 수 있다. Deep Q-Network (DQN)은 냉방 시스템의 동적 거동을 학습하고 건물에 냉방을 공급하는 동시에 에너지 사용을 줄일 수 있는 제어 전략을 모색하는 데 적용된다. DQN의 제어 성능을 현재 건물 운영자들이 적용하는 기존 제어 성능과 비교함으로써 RL 제어기가 시스템의 제어 효율성을 크게 개선할 수 있으며 연합 모델은 강화학습 기반 제어기의 학습을 위한 가상 환경을 제공할 수 있음을 증명한다. DQN 에이전트의 해석성을 높이기 위해 의사결정 트리를 사용하여 에이전트의 의사결정 프로세스에 대한 설명을 추출한다. 에이전트에서 생성된 상태-작업 (state-action) 쌍이 의사결정 트리를 훈련하는 데 사용된다. 얕지만 쉽게 해석할 수 있는 모델을 사용한 사후 해석은 강화 학습의 투명성과 해석성을 향상시킨다. 또한 의사결정나무가 만든 분류 결과는 인공지능이 만든 제어 전략을 단순화시킨 'If-then' 규칙을 도출한다. 추출된 규칙 (reduced rule) 기반 제어의 성능과 DQN 제어기의 성능을 비교하여 두 제어기 사이의 에너지 절약량 차이가 2.8%로 미미함을 보인다. 즉, 규칙 기반 제어가 충분한 성능을 보인다는 것을 증명한다. 본 연구는 기축 사무실 건물의 냉방 제어를 위한 설명 가능한 RL의 적용 방안에 대해 수행된다. 의사 결정 트리를 훈련된 DQN 에이전트에 적용한 다음 일련의 단순화된 제어 규칙을 도출한다. 이 연구는 설명 가능한 강화학습을 이용한 정량화된 규칙 도출 프레임워크를 제안하고, 복잡한 강화학습 알고리즘과 비교하여 단순하지만 정량적인 평가가 수행된 규칙이 충분한 성능을 보여줄 수 있음을 보여준다. 이 연구의 의의는 건물 통제에 대한 정량적 평가를 통해 규칙을 도출하는 방법을 제안하는 데 있다.Building controls are becoming complicated because modern building systems must respond to not only conventional systems like HVAC and lighting, but also to novel systems such as intermittent renewables, energy storage systems, and more. Therefore, the advanced building controllers must balance the trade-off between multiple objectives and automatically adapt to dynamic environment. Although it is widely acknowledged that reinforcement learning (RL) can be beneficially used for better building control, there are several challenges that should be addressed for real life application of RL: (1) unstable and poor control actions during early training period of RL may cause unexpected costs; (2) many RL-based control actions still remain unexplainable for daily practice of facility managers. By applying RL algorithms as artificial intelligences that are the subject of decision-making, owners and operators of buildings need to be reassured about the controllers intentions. To address the first challenge, federated model, a novel concept of simulation model, is proposed for pre-training RL agents. The federated model is an integrated data-driven model that divides a building system into several modules based on physical causality and develops each module into a data-driven model to perform simulations on building systems. A federated model of a complex cooling system of a target building is realized using six modules, each developed using data gathered from BEMS. By developing the federated model, limitations of physics-based simulation models (eg. topology rules, model calibration) are overcome. Deep Q-network (DQN) is applied to learn the dynamics of the cooling system and explore control strategies that can reduce energy use while providing cold for the building. By comparing the control performance of DQN with the performance of baseline control, it is shown that RL controller can significantly enhance control efficiency of the system and the federated model can provide sufficient virtual experience for the controller. To enhance interpretability of the DQN agent, decision tree is used to extract explanation of the decision making process of the agent. State-action pairs generated by the agent is used train a decision tree. Post-hoc interpretation using a shallow but easily interpretable model enhances transparency and interpretability of reinforcement learning. Also, the result of classification made by the decision tree provides If-then rules which are reduced version of control strategies made by the artificial intelligence. The performance of the reduced rule-based control is also compared to the performance of DQN controller. It is demonstrated that the reduced rule is good-enough and the difference in energy savings between the two is marginal, resulting in 2.8%. This study reports the development of explainable RL for cooling control of an existing office building. A decision tree is applied to trained DQN agent and then a set of reduced-order control rules are suggested. This study proposes rule reduction framework using explainable reinforcement learning and demonstrates that reduced rules can perform as well as complex reinforcement learning algorithms. The significance of this study lies in proposing how to derive rules with quantitative evaluation for building control.1. Introduction 1 1.1 Control of building systems 1 1.2 Problem Description 2 1.3 Goal 4 1.4 Thesis Outline 5 2. Deep Q-network (DQN) 7 2.1. Summary of reinforcement learning 7 2.1.1 Elements of reinforcement learning 7 2.1.2 Value function 9 2.2. Deep Q-learning 12 2.2.1 Temporal difference (TD) learning and Q-learning 12 2.2.2 Deep Q-learning 14 2.3. Previous works to implement reinforcement learning to existing buildings 16 2.4. Conclusion 19 3. Decision Trees 21 3.1 Summary of decision tree 21 3.2 Classification And Regression Trees (CART) 23 3.3 Interpreting reinforcement learning using decision tree 24 3.4 Conclusion 26 4. Target building and Federated model 27 4.1 Parallel cooling system 27 4.2 Federated model 31 5. Explainable deep Q-network and rule reduction for building control 40 5.1 DQN implementation framework 40 5.2 Control results of DQN 46 5.3 Rule reduction from DQN agent 50 5.4 Discussion 54 6. Conclusion 55 6.1 Summary and conclusion 55 6.2 Future works 57 Reference 58석

The Potential of Liquid-Based BIPV/T Systems and Ice Storage for High Performance Housing in Canada

ASHRAE Vision 2020 has defined market viable net-zero energy buildings as a key objective for new construction in North America. Designing for this target requires the effective integration of renewable energy systems into the building. However, many buildings have limited roof and façade areas in which to integrate these systems, making it difficult to achieve a net zero energy design. Building Integrated Photovoltaic and Thermal (BIPV/T) offers a potential solution to this issue by converting the building envelope into an active producer of both thermal and electrical energy. Commonly, BIPV/T systems in North America have used air as a working fluid. While this offers easy integration with the building ventilation system, air also has a lower thermal capacitance, reducing thermal energy extracted from a BIPV/T collector. Liquid based systems offer working fluids with higher thermal capacitance, along with the ability to easily integrate with existing thermal storage systems. However, these systems often circulate warm water in order to directly meet heating and hot water loads, resulting in reduced thermal and electrical efficiencies and less durable BIPV/T modules. Circulating cooler water to the collectors can significantly improve both the thermal and electrical efficiencies of liquid based BIPV/T systems. However, the low grade thermal energy collected must then be upgraded for use within the building. This paper examines the potential of using liquid based BIPV/T systems with cool storage and heat pump technologies to meet the thermal demands of a high performance Canadian home. An innovative liquid based BIPV/T system is proposed in which the collector array is connected to a cool storage tank, while a heat pump is used to upgrade and deliver thermal energy to the building. Both sensible and ice-based latent storage options are examined as cool storage possibilities. To perform the analysis, TRNSYS is used to simulate the proposed system integrated into a high performance home in Montreal, Canada. Annual simulation results are presented and compared with typical base case designs. A more detailed temporal analysis of electrical loads is also performed in order to examine the impact of the proposed system on the electricity grid.