Demonstrating a smart controller in a hospital integrated energy system

Integrated energy systems have recently gained primary importance in clean energy transition. The combination of the electricity, heating and gas sectors can improve the overall system efficiency and integration of renewables by exploiting the synergies among the energy vectors. In particular, real-time optimization tools based on Model Predictive Control (MPC) can considerably improve the performance of systems with several conversion units and distribution networks by automatically coordinating all interacting technologies. Despite the relevance of several simulation studies on the topic, however, it is significantly harder to have an experimental demonstration of this improvement. This work presents a methodology for the real-world implementation of a novel smart control strategy for integrated energy systems, based on two coordinated MPC levels, which optimize the operation of all conversion units and all energy vectors in the short- and long-term, respectively, to account also for economic incentives on critical units. The strategy that was previously developed and evaluated in a simulation environment has now been implemented, as a supervisory controller, in the integrated energy system of a hospital in Italy. The optimal control logic is easily actuated by dynamically communicating the optimal set-points to the existing Building Management System, without having to alter the system configuration. Field data collected over a two-year period, firstly when it was business as usual and when the new operation was introduced, show that the MPC increased the economic margin and revenues from yearly incentives and lowered the amount of electricity purchased, reducing dependency on the power grid

Setpoint Tracking Predictive Control in Chemical Processes Based on System Identification

A Kraft recovery boiler in a pulp-paper mill provides a means for recovery of the heat energy in spent liquor and recovery of inorganic chemicals while controlling emissions. These processes are carried out in a combined chemical recovery unit and steam boiler that is fired with concentrated black liquor and which produces molten smelt. Since the recovery boiler is considered to be an essential part of the pulp-paper mill in terms of energy resources, the performance of the recovery boiler has to be controlled to achieve the highest efficiency under unexpected disturbances. This dissertation presents a new approach for combining system identification technique with predictive control strategy. System identification is the process of building mathematical models of dynamical systems based on the available input and output data from the system. Predictive control is a strategy where the current control action is based upon a prediction of the system response at some number of time steps into the future. A new algorithm uses an i-step-ahead predictor integrated with the least-square technique to build the new control law. Based on the receding horizon predictive control approach, the tracking predictive control law is achieved and performs successfully on the recovery boiler of the pulp-paper mill. This predictive controller is designed from ARX coefficients that are computed directly from input and output data. The character of this controller is governed by two parameters. One parameter is the prediction horizon as in traditional predictive control and the other parameter is the order of the ARX model. A recursive version of the developed algorithm can be evolved for real-time implementation. It includes adaptive tuning of these two design parameters for optimal performance. The new predictive control is proven to be a significant improvement compared to a conventional PID controller, especially when the system is subjected to noise and disturbances

The potential and challenges of monitoring-supported energy efficiency improvement strategies in existing buildings

The ongoing EU-supported CAMPUS 21 explores the energy efficiency potential of integrated security, control, and building management software. The main objective of the project is to compare the energy and indoor-environmental performance of a number of existing facilities before and after real or virtual implementation of monitoring-based control improvement measures

Do domestic heating controls save energy? A review of the evidence

© 2018 The Authors Claims about the benefits of heating controls are often biased, unsubstantiated, misleading, or incorrect. This paper presents a systematic and critical international review of the evidence for the energy saving, cost effectiveness and usability of heating controls. The focus is domestic, low-pressure hot water heating systems in temperate climates. Eleven different types of standard, advanced and smart controls are assessed plus five components and features that add smart functionality. The review retrieved over 2400 documents from on-line databases and other sources. Screening criteria and quality assurance scoring identified just 67 items, mainly from the UK and USA, which appeared to contain relevant evidence. This evidence was derived from computer modelling, field trials and full-scale experiments, and for usability, from expert evaluations and controlled assessments. The evidence was synthesised and its quality classified as very low, low, moderate or high using the GRADE system which is more commonly applied in evidence-based medicine. The energy savings of most heating controls depends strongly on whether the heating system is operated with a continuous or periodic heating pattern, as well as on the energy efficiency of the dwelling and the severity of the climate. For most control types, the quality of the evidence for energy savings was low, very low or non-existent. However, there was moderate quality evidence that, when appropriately commissioned, zonal controllers, which heat individual spaces to different temperatures at different times, could save energy compared to whole-house controllers, and that low-cost systems of this type could be cost-effective. There was moderate quality evidence that smart thermostats do not save energy compared to standard thermostats and programmers and may, in fact, increase energy demand. The usability studies focussed on general heating controls and programmable thermostats and provided high quality evidence that heating controls are difficult to use, especially by older people. However, no studies were uncovered that quantified the consequent energy penalty. There was no high quality evidence about the impact on energy demand of any of the heating controls studied, mainly because there have been no well-founded, large-scale, multi-disciplinary, multi-year field trials

Tilalämmityksen kysyntäjousto mallipohjaisella algoritmilla toimistorakennuksessa

Decreasing the CO2 emissions of building stock plays a remarkable role in the mitigation of global warming. The share of building sector from both the global final energy use and CO2 emissions is about 30%. Demand response of electricity and district heating provides one tool for decreasing emissions in the whole energy system. In demand response the buildings energy use is controlled so that the peak-load consumption in the energy grid decreases and the consumption profile stabilizes. CO2 emissions are reduced since the need for emission-intensive peak-demand generation decreases. The building owners benefit from the energy cost savings and the energy producers from the higher grid efficiency and decreased investments for peak-demand power plants. The main objective of this thesis was to define the potential of space heating demand response in the perspective of local thermal comfort, cost savings and energy flexibility. Demand response was implemented using a model predictive control algorithm (MPC) that optimized and controlled the space heating temperature setpoints. The MPC algorithm was tested with dynamical simulation model of an educational office building located in Aalto University campus area. The second research question was to examine how the demand response of space heating affects the local thermal comfort of occupants. The draught risk during the demand response was investigated by thermal manikin measurements in workstations near windows. To prevent the draught risk, a window surface temperature restriction was implemented in the MPC control algorithm and its influence on the demand response potential was investigated with different properties of windows. The thermal comfort measurements showed that the draught risk increased in workstations adjacent to windows during the decreased heating power. The increase in draught risk was noticed when the window surface temperature dropped below 15 °C while the heating was turned OFF. The influence from the window surface temperature restriction on the demand response potential was found to be small. With energy efficient windows, the influence was negligible and with non-energy efficient windows the demand response potential was affected only when unnecessary high power requirements were set. Using the MPC algorithm, the annual heating cost of the case building could be decreased 4.7%. The highest energy flexibility obtained was 14%.Rakennusten hiilidioksidipäästöjen vähentämisellä voidaan edistää merkittävästi ilmastonmuutoksen torjumista, sillä rakennusten osuus kokonaisenergiankulutuksesta (ja hiilidioksidipäästöistä) maailmassa on noin 30%. Sähkön ja lämmön kysyntäjousto rakennuksissa on yksi keino koko energiajärjestelmän kasvihuonepäästöjen vähentämiseen. Kysyntäjoustossa kuluttajat muuttavat kulutustaan siten, että energiaverkon huipputehon tarve laskee ja kulutuksesta tulee stabiilimpaa. Kysyntäjousto vähentää kasvihuonepäästöjä, sillä energia- ja päästöintensiivisiä huippuvoimalaitosten käyttötarve vähenee. Kysyntäjoustosta on hyötyä rakennusten omistajille kustannussäästöjen muodossa ja energiayhtiöille investointitarpeen pienenemisenä sekä verkon hyötysuhteen paranemisena. Tämän tutkimuksen tavoitteena oli tutkia tilojen lämmityksen kysyntäjoustopotentiaalia kustannussäästöjen, energiankäytön joustavuuden ja lämpöviihtyvyyden näkökulmasta. Lämmityksen kysyntäjousto toteutettiin tilojen lämmitystä ohjaavan mallipohjaisen algoritmin avulla. Algoritmia testattiin Aalto yliopiston kampusalueella sijaitsevaan opetusrakennukseen dynaamisen simulointityökalun avulla. Toisena tutkimuskysymyksenä oli selvittää millainen vaikutus lämmityksen kysyntäjoustolla on lokaaliin lämpöviihtyvyyteen. Tässä työssä kysyntäjouston vaikutusta vetoriskiin tutkittiin kokeellisesti lämpönuken avulla työpisteissä, jotka sijaitsivat ikkunoiden lähellä. Kylmistä ikkunapinnoista johtuvan vetoriskin välttämiseksi kysyntäjoustolle asetettiin rajoite mallipohjaisessa algoritmissa, jonka vaikutusta kysyntäjoustopotentiaaliin tutkittiin erilaisilla ikkunoiden ominaisuuksilla. Kokeelliset lämpöviihtyvyysmittaukset osoittivat, että vetoriski ikkunoiden lähellä sijaitsevissa toimistopisteissä kasvaa, kun pattereiden tehoa lasketaan kysyntäjouston aikana. Vetoriskin huomattiin kasvavan, mikäli ikkunan pintalämpötila laski alle 15 °C, kun patterit eivät olleet päällä. Vetoriskin pienentämiseksi tehdyn rajoitteen vaikutus kysyntäjoustolla saavutettaviin kustannussäästöihin sekä energiajoustavuuteen huomattiin olevan pieni. Energiatehokkailla ikkunoilla vaikutus kysyntäjoustopotentiaaliin oli mitätön, ja huonoilla (U-arvo = 2,6 W/m2K) ikkunoilla potentiaali laski vasta tarpeettoman suurilla lämmitystehon korotuksilla. Mallipohjaisen algoritmin avulla tutkitun toimistorakennuksen vuotuisia lämmityskustannuksia pystyttiin vähentämään noin 4.7%. lämmityksen joustavuudeksi saatiin parhaassa tapauksessa 14%

Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings.

This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the “Autonomous Cycle of Data Analysis Tasks” concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.post-print4413 K

Integrated smart system for energy audit: methodology and application

Abstract The article describes the design and the application stage of a smart energy audit system, integrated within building, and the methodologies adopted for the detection of malfunctions of the plant. The system is set up as a "black box" consisting of a hardware aimed at logging both energy and environmental parameters and a software for the assessment of building behavior and the management of energy flows. The Energy Signature was chosen as the reference method for the evaluation of the energy performance of building. The system was tested in an existing public office building

Heating controls scoping review project

This report summarises the findings of an evidence review of the energy savings, cost-effectiveness and usability of different types of heating controls