Modelling of Wastewater Heat Recovery Heat Pump Systems

Wastewater heat recovery is currently an underutilized technology that could be part of solving the climate crisis. A large portion of the heat that leaves a building in the form of wastewater is potentially recoverable for pre-heating domestic hot water or other service water systems. While there are several different approaches to wastewater heat recovery, this project focused on creating detailed, integrated building models for wastewater heat recovery heat pump systems. EnergyPlus models were developed featuring inputs and assumptions corresponding to manufacturers’ specifications, performance lab test data and feedback from engineering consultants. EnergyPlus’s supervisory control Energy Management System objects were heavily relied upon to overcome modelling challenges. The developed EnergyPlus model was integrated into U.S. Department of Energy New Construction Reference Building models for various climate zones and building types to assess potential energy use, energy cost and greenhouse gas emission reductions

CONVERGING REDUNDANT SENSOR NETWORK INFORMATION FOR IMPROVED BUILDING CONTROL

Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Analysis tools based on Bayesian probability theory were applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice

Optimal Dispatch Controller For Fuel Cell Integrated Building

Buildings contribute to around 40% of the total energy consumption in the US. Improvements to building operation offer substantial economic benefits and emissions reductions. Opportunities arise as more renewable energy sources are integrated into the power grid, where the inherent flexibility that buildings can provide become valuable assets for grid services. Stationary fuel cells providing combined heat and power (CHP) add more flexibility to building operation, where both significant electrical and thermal loads need to be met. As the technology matures, improved fuel cell responsiveness allows for advanced dynamic applications to maximize their utility within the building system. The integration of fuel cells and battery energy storage systems (BESS) to buildings presents several challenges and opportunities for optimal management of resources. In this work, we develop an optimal dispatch controller for real-time management of a fuel cell-integrated building system. The objective is to minimize building operating costs and maximizing profits from participating in the power grid ancillary service markets, while maintaining occupant comfort. To achieve this objective, we develop a specifically tailored model predictive control (MPC) algorithm to schedule the operation of a fuel cell, a BESS, and building equipment in response to the time-of-use electricity tariff. The controller determines the optimal schedules over a 24-hour horizon according to weather and building load forecast. This optimal schedule is implemented for a 1-hour period. Measurements from the fuel cell-integrated building are collected and used to update the optimization for the next 24-hour period. This recursive update ensures that the algorithm is robust to forecast errors and model mismatch. The effectiveness of the proposed algorithm is demonstrated with a co-simulation where the building is represented as a high-fidelity model in the EnergyPlus building simulation program and the optimal control is implemented in Matlab. The proposed optimal dispatch controller provides a tool to manage the real-time operation of a fuel cell-integrated building. It also helps building operators and the fuel cell industry assess the potential benefits of integrating stationary fuel cells with buildings

Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control

District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.</div

Ten questions concerning integrating smart buildings into the smart grid

Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be ‘prosumers’ on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and non-technical areas

Ten questions concerning energy flexibility in buildings

Funding Information: The authors are key collaborators in the IEA EBC Annex 82 project. Dr. Li leads IEA EBC Annex 82 “Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems.” Mr. Satchwell researches utility regulatory and business models that achieve greater deployment of energy efficiency, demand flexibility, and other distributed energy resources. Prof. Finn investigates demand response measures in the residential and commercial building sectors. Senior researcher Christensen researches the role of users in smart energy solutions and low-carbon energy transitions. Prof. Michaël Kummert's research focuses on modeling and control of building-scale and community-scale energy systems to optimize energy flexibility and resilience. Dr. Le Dréau researches energy flexibility of buildings both at building and district scales, develops occupant behavior models and prediction techniques related to flexibility. Dr. Lopes is involved in two international projects funded by the European Union's H2020 programme where he is developing and applying energy flexibility characterization methodologies and optimization algorithms in several demonstration activities. Prof. Madsen leads a national research project ‘Energy Flexible Denmark’ and he focuses on grey-box modeling, digital twins, forecasting and control for smart buildings in smart grids. Dr. Salom research works focus on zero/positive energy buildings and districts and their interaction with energy infrastructures being involved in several international projects. Prof. Henze researches model predictive and reinforcement learning control and data analytics for the integration of building and district energy systems with the electric grid. Mr. Wittchen research works focus on zero/positive energy buildings and districts and implementation of European legislation on building's energy performance. Funding Information: The authors acknowledge the many organizations that directly or indirectly supported the completion of this article. We acknowledge the European Commission for the ARV (grant number 101036723 ), Syn.ikia (grant number 869918 ), Hestia (grant number 957823 ) projects; the Danish Energy Agency for supporting the Danish delegates participating IEA EBC Annex 82 through EUDP (grant number 64020-2131 ); Innovation Fund Denmark in relation to SEM4Cities ( IFD 0143–0004 ) and Flexible Energy Denmark ( IFD 8090-00069B ); the Building Technologies Office, Office of Energy Efficiency and Renewable Energy, at the US Department of Energy , under Lawrence Berkeley National Laboratory (contract number DE-AC02-05CH11231 ); the Center of Technology and Systems (CTS UNINOVA) and the Portuguese Foundation for Science and Technology (FCT) through the Strategic Program UIDB/00066/2020 ; Research Council of Norway in relation to Research Centre on Zero Emission Neighborhoods in Smart Cities - FME-ZEN (No. 2576609 ) and FlexBuild (No. 294920 ); the AGAUR Agency from the Generalitat de Catalunya through the project ComMit-20 ( 2020PANDE00116 ); the National Science and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN 2016-06643 ). Publisher Copyright: © 2022 The AuthorsDemand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings.publishersversionpublishe

The TESS Grand Unified Hot Jupiter Survey. I. Ten TESS Planets

We report the discovery of ten short-period giant planets (TOI-2193A b, TOI-2207 b, TOI-2236 b, TOI-2421 b, TOI-2567 b, TOI-2570 b, TOI-3331 b, TOI-3540A b, TOI-3693 b, TOI-4137 b). All of the planets were identified as planet candidates based on periodic flux dips observed by NASA's Transiting Exoplanet Survey Satellite (TESS). The signals were confirmed to be from transiting planets using ground-based time-series photometry, high angular resolution imaging, and high-resolution spectroscopy coordinated with the TESS Follow-up Observing Program. The ten newly discovered planets orbit relatively bright F and G stars ($G < 12.5$,~$T_\mathrm{eff}$ between 4800 and 6200 K). The planets' orbital periods range from 2 to 10~days, and their masses range from 0.2 to 2.2 Jupiter masses. TOI-2421 b is notable for being a Saturn-mass planet and TOI-2567 b for being a ``sub-Saturn'', with masses of $0.322\pm 0.073$ and $0.195\pm 0.030$ Jupiter masses, respectively. In most cases, we have little information about the orbital eccentricities. Two exceptions are TOI-2207 b, which has an 8-day period and a detectably eccentric orbit ($e = 0.17\pm0.05$), and TOI-3693 b, a 9-day planet for which we can set an upper limit of $e < 0.052$. The ten planets described here are the first new planets resulting from an effort to use TESS data to unify and expand on the work of previous ground-based transit surveys in order to create a large and statistically useful sample of hot Jupiters.Comment: 44 pages, 15 tables, 21 figures; revised version submitted to A

“Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory”

Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building’s massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigates the merits of harnessing both storage media concurrently in the context of predictive optimal control. The analysis, modeling, and simulation research presented in this topical report covers the first of three project phases. Based on the new dynamic building simulation program EnergyPlus, we added a utility rate module, two thermal energy storage models, and incorporated a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods. The objective function is the total utility bill including the cost of heating and a time-of-use electricity rate with demand charges. The evaluation of the combined optimal control assumes perfect weather prediction and match between the building model and the actual building counterpart. The analysis shows that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be drastically reduced and justify the development of a predictive optimal controller that accounts for uncertainty in predicted variables and modeling mismatch in real time

Interactive Buildings: A Review

Buildings are widely regarded as potential sources for demand flexibility. The flexibility of thermal and electric load in buildings is a result of their interactive nature and its impact on the building’s performance. In this paper, the interaction of a building with the three interaction counterparts of the physical environment, civil infrastructure networks and other buildings is investigated. The literature review presents a wide variety of pathways of interaction and their associated potential impacts on building performance metrics such as net energy use, emissions, occupant comfort and operational cost. It is demonstrated that all of these counterparts of interaction should be considered to harness the flexibility potential of the buildings while maintaining other buildings performance metrics at a desired level. Juxtaposed with the upside potential for providing demand flexibility, numerous implementation challenges are identified that are associated with the evaluation and financial valuation of the capacity for demand flexibility, the aggregated flexibility potential, as well as the control and communication to facilitate the interactions