Optimization of a District Heating energy supply system under a cost-effectiveness perspective

xi, 62 p.The necessity to reduce greenhouse gas (GHG) emissions is a world global challenge that is reflected in numerous international, agreements, national and local regulations. Build-ings stand out as one of the sectors that require a significant amount of energy, hence building efficiency optimization and incorporation of renewable energy sources (RES) is a constant tendency aimed at leading to the creation of concepts, such as nearly Zero Emission Buildings (nZEB). In this respect, the potential for reducing energy consumption in the building stock evolving towards districts scale, which can play a significant role in the energy transition of the stock, as its tackle larger scale of projects. To this end, along with passive energy efficiency measures (EEM) district heating (DH) is one of the options for the reduction of energy consumption and emissions from heat production. The thesis concerns optimization of the district heating network under the cost-effec-tivity analysis of centralized energy supply systems (ESS) in conjunction with passive EEM, and RES for the Otxarkoaga neighbourhood in Bilbao. For this purpose, based on a simplified model (131 buildings) of the neighbourhood created in the Design Builder program the set of ESS scenarios for the DH network has been designed, described, and simulated. Following that, simulation output has been analysed by energy demand and ESS consumption within a set of applied EEM at the district level. In total 60 combina-tions have been considered. Also, the DH network topology was proposed, and distribu-tion heat losses were characterized. Finally, the economic study of considered ESS tech-nologies of DH network analyzed under the cost-effectiveness perspective and compara-tive characteristics of the district to individual building renovation is carried out. The study showed that centralised heating could be cost-effective for a considered neighbourhood within some technologies. The biomass boiler ESS can have renovation combination with the lowest investment in 10,000 € per building, and geothermal solution, being highly investment, can reach the lowest energy consumption

Development of a shoeboxing approach for Urban Building Energy Modeling

Urban Building Energy Modeling aims at assessing the building energy performance at city scale with as little computational effort as possible. Thus, different methods have been developed in the last years to reduce the required calculation time by simplifying the modeling approach, selecting only representative buildings, or minimizing the building description. Starting from the latter ones, this work proposes a novel algorithm capable of abstracting a randomly shaped building into a representative shoebox. The presented shoebox generation algorithm is based on a preliminary sensitivity screening analysis on a set of reference parallelepiped-shaped thermal zones. This allowed the identification of the most significant geometry indicators influencing the building’s performance. Based on this, more complex geometries have been simplified to the shoebox with the same indicators and the accuracy of the algorithm has been evaluated comparing the simulated performance of simplified and original buildings. The approach includes the definition of equivalent shading surfaces, to account for self-shading elements in the original building geometry. The algorithm has shown good accuracy not only on the hourly thermal loads, but also the zones’ hourly temperature profiles, reducing to one third the energy simulation time with respect to the detailed building model. Although not as fast as other urban modelling approaches in the literature, it can retain accurate results at a finer time scale, i.e., on hourly basis, which is necessary in applications such as district heating and energy networks

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Kysyntäjouston ohjausstrategioiden optimointi suomalaisissa kaupungin omistamissa kiinteistöissä

The entire energy business from producers to energy end-users is currently undergoing major reforms due to more and more ambitious targets for climate change mitigation measures and energy efficiency of buildings stemming from various international agreements and dwindling of conventional fossil fuel resources. Both supply and demand side measures are required to tackle the issues at hand and much work has already been done in regards to developing and increasing renewable energy generation and demand side energy efficiency. Demand response is a more novel demand side action which targets reducing energy demand during peak demand hours, which in turn can reduce the need for expensive peak production and contribute to increasing the stability of the grid when system reliability is jeopardized. In practice, demand response means that energy use is changed from its typical patterns when it is beneficial from the relevant parties’ point of view. This thesis investigates heat load reduction potential for demand response purposes in typical Finnish city-owned district heated buildings. The potential is analyzed for three different types of buildings individually (office, school and apartment building) and on a city scale for a certain city located in southern Finland by creating building energy models for example buildings in the simulation software IDA ICE and optimizing demand response control strategies in the optimization software MOBO. MOBO is used to determine an optimal combination of controls for these strategies in terms of maximum direct cost saving potential resulting from reduced energy consumption. The optimizations are conducted for a few different example days in winter and in spring, and for a single three-hour-long demand response event on these days. Furthermore, the district heat producer’s point of view is regarded by using hourly marginal cost based district heat pricing as one of the minimized objectives in the optimizations. Hourly heat production costs and marginal costs before and after demand response implementation are calculated for the studied city in a previously developed MATLAB simulation model. The results of the simulations and optimizations indicate that heat load reduction potential for demand response in individual buildings is 50-80% for a single demand response event during the day and depending on the building type. On a city-scale, the achieved heat load reduction is 59 MW or 60-70% of the original heat demand at most, which accounts for approximately 10% of the heat demand of the entire city at the time.Energiateollisuus ja energiajärjestelmät tuottajista loppukäyttäjiin ovat tällä hetkellä keskellä merkittävää uusiutumista ja suuria muutoksia johtuen yhä kunnianhimoisemmista kansainvälisistä ilmastotavoitteista ja jatkuvasti tiukkenevista kansallisista energiatehokkuusmääräyksistä. Muutokset koskevat sekä tuottajia että kuluttajia, ja paljon työtä on jo tehty liittyen uusiutuvien energiamuotojen kehittämiseen ja käytön lisäämiseen sekä kuluttajapuolenkin energiatehokkuuteen. Kysyntäjousto on eräs vähemmän yleistynyt kuluttajapuolen toimintamalli, jolla pyritään vähentämään energiankulutusta kulutuspiikkien aikana, jolloin myös kalliin huipputuotannon tarve vähenee, ja parantamaan tarvittaessa systeemin tasapainoa sen ollessa uhattuna. Käytännössä kysyntäjousto tarkoittaa energiankäytön hetkellistä muuttamista normaalitilanteesta sen ollessa kysyntäjoustoon osallistuvien osapuolten kannalta edullista. Tässä diplomityössä tutkitaan kaukolämmön kysyntäjoustopotentiaalia lämmitystehon pienentämisen kannalta tyypillisissä Suomen kaupunkien omistamissa kiinteistöissä. Potentiaalia tutkitaan kolmessa erilaisessa rakennuksessa (toimisto, asuinkerrostalo ja koulu) yksitellen sekä koko kaupungin tasolla eräässä Etelä-Suomen kaupungissa. Esimerkkirakennuksista luodaan energiasimulointimallit IDA ICE – ohjelmalla, jonka jälkeen MOBO-optimointityökalulla määritetään erilaisista talotekniikkaohjauksista koostuva optimaalinen kysyntäjoustokombinaatio, jolla voidaan saavuttaa suurimmat energiankäytön vähenemisestä johtuvat kustannussäästöt kiinteistönomistajan sekä kaukolämpöyhtiön kannalta. Optimointitapaukset tehdään esimerkinomaisille talvi- ja kevätpäiville, joina kumpanakin toteutetaan yksi kolmen tunnin pituinen kysyntäjoustojakso aamupäivän aikana. Kaukolämmön tuottajan näkökulmaa pyritään tuomaan esille käyttämällä yhtenä optimoitavan tekijänä kaukolämmön kuluttajahintana käytettäviä lämmöntuotannon tuntikohtaisia marginaalikustannuksia. Tuntikohtaiset tuotantokustannukset ja marginaalikustannukset ilman kysyntäjoustoa ja sen kanssa määritetään MATLAB simulointimallia hyväksi käyttäen. Simulointien ja optimointien tulosten perusteella kaikilla kolmella rakennustyypillä on selvää tehonleikkauspotentiaalia kysyntäjoustotarpeisiin. Yksittäisille rakennuksille tehon alenema yksittäisen kysyntäjouston aikana on 50-80% alkuperäisestä kaukolämpötehosta. Koko kaupungin tasolle skaalattuna tämä tarkoittaa yhteensä maksimissaan 59 MW:n kaukolämpötehon leikkausta, joka on 60-70% alkuperäisestä näiden rakennustyyppien koko kaupungin omistaman rakennusmassan tehosta ja yhteensä noin 10% koko kaupungin kyseisen hetken kaukolämmön tarpeesta

Optimization of Sustainable Urban Energy Systems: Model Development and Application

Digital Appendix: Optimization of Sustainable Urban Energy Systems: Model Development and Applicatio

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques