An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results

This paper presents a holistic innovative solution for the transformation of the current district heating and cooling systems to automated more efficient systems. A variety of technological advancements have been developed and integrated to support the effective energy management of future district heating and cooling sector. First, we identify and discuss the main challenges and needs that are in line with the EU objectives and policy expectations. We give an overview of the main parts that our solution consists of, with emphasis on the forecasting tools and an advanced control system that addresses unit commitment and economic load dispatch problems. The proposed control approach employs distributed and scalable optimisation algorithms for optimising the short-term operations of a district heating and cooling plant subject to technical constraints and uncertainties in the energy demand. To test the performance and validate the proposed control system, a district heating plant with multiple energy generation units and real-life heat load data were used. Simulation experiments were also used to evaluate the benefits of using thermal storage units in district heating systems. The results show that the proposed method could achieve significant cost savings when energy storage is employed. The proposed control strategy can be applied for both operating optimally district heating plants with storage and supporting investment planning for new storage units

Modelling and Simulation of the Fifth-Generation District Heating and Cooling

District heating and cooling are efficient systems for distributing heat and cold in urban areas. They are a key solution for planning future urban energy-efficient systems due to their high potential for integrating renewable energy sources. The systems also play an important role in community resilience, which makes them a multidisciplinary research topic. The continuous development of these systems has now reached the fifth-generation whereby end-customers can benefit from the intrinsic synergies this generation offers. A typical Fifth-Generation District Heating and Cooling (5GDHC) system consists of connected buildings that together have simultaneous heating and cooling demands. Local heat pumps and chillers in decentralised substations modulate the low network temperature to the desired building supply temperatures. The demands are potentially balanced by the means of recovering local waste heat from chillers, while also utilising heat pumps to provide direct cooling. The heat carrier fluid in the distribution pipes can therefore flow in either direction in the so-called bidirectional low-temperature network. A balancing unit is incorporated to compensate for network energy imbalances. The exchange of energy flows is realised at different stages within the individual building and across connected buildings. Numerous factors influence the quantity and quality of the exchanged energy flows. Demand profiles in each building, the efficiency of building energy systems, and control logics of system components are some examples of these factors. Investigating this generation using traditional computational tools developed using imperative programming languages is no longer suitable due to system complexity, size variability, and changes adopted in different use cases. Modelica is a free open-source equation-based object-oriented language used for the modelling and simulation of multi-domain physical systems. Models are described by differential-algebraic and discrete equations. The mathematical relations between model variables are encapsulated inside an icon that represents the model. Different component models interface variables through standardised interfaces and connection lines. Large complex systems are composed by the visual assembly of components in a Lego-like approach. Models developed in Modelica can be easily inherited for rapid virtual prototyping and/or edited to adopt changes in the model use. This dissertation has a fourfold objective. Firstly, it demonstrates the development of a simulation model for an installed 5GDHC system located in Lund, Sweden. Secondly, it characterises the components that constitute a 5GDHC system. Thirdly, it unravels the exchange of energy flows at different system levels and describes, in a logical progression, the modelling of 5GDHC with Modelica. Fourthly, it presents ethical risk analyses of the different role-combinations that may arise in 5GDHC business models. The developed model is used in performing annual simulations and to evaluate the system performance under two different substation design cases. The results indicate that adding a direct cooling heat exchanger in each substation can reduce the electric energy consumption at both substation and system levels by about 10 and 7 %, respectively. Moreover, the annual waste heat to ambient air can be decreased by about 17 %. The dissertation fosters an ethical discourse that engages the public and all who take part in the multidisciplinary research on 5GDHC to guarantee safe operation and appropriate services. Future research will build on the models presented in this dissertation to investigate different network temperature and pressure control strategies, in addition to adopting several design concepts for balancing units and thermal energy storage systems

An Asynchronous Replication Model to Improve Data Available into a Heterogeneous System

Data replication is an important technique for high level data availability, especially applied for distributed systems. To enhance the data access and data reliability, data replication is one of the most important aspects. This paper is proposed a asynchronous replication model. This model also supports heterogeneous system which is currently a very promising system. In our proposed model the main server and replication servers are loosely coupled. Since the structure is loosely coupled and asynchronous model thus there is less dependency on each server. Moreover the proposed model is supporting heterogeneous system, so it will be highly cost minimizing solution for efficient data replication. Keywords: Data replication, Data Persistency, Asynchronous, Heterogeneous Replication

Ennustava kysyntäjousto kaukolämmitetyissä ja -jäähdytetyissä kiinteistöissä

Contemporary technologies enable the control of energy consumption in buildings in a way which minimizes costs and maximizes consumer comfort. Cost reductions have been seen in District Heating and Cooling systems mostly as energy conservation. On the other hand, consumer comfort is increased by providing steadier indoor temperatures. Lately, literature present has presented a more agile approach of reducing costs by optimizing the consumption of the overall system with Demand-side Management. This approach aims to shift loads from peak load hours without necessarily reducing energy consumption. This thesis provides a model which applies the thermal capacity of District Heating and Cooling connected buildings as thermal energy storages. An artificial District Heating system with variable production costs was developed for the model in order to form dynamic price signals. These signals were utilized in two simulations. The first optimized existing heat load data from heavy mass buildings to appraise the effect of Demand-side Management, whereas the second modelled the behaviour of individual rooms. Both simulations aimed to store heat beforehand in the building envelope and to discharge it during price peaks. This offered the possibility to consume heating energy based on individual consumption profiles and only take action when the whole system requires it. The simulation model indicates that predictive Demand-side Management with dynamic price signals reduces heating costs in buildings by 4% during the heating period. The main cost savings occur to energy producers since variable production costs can be decreased by 6% due to load control using 15% of the building stock’s heated floor area. The room simulation demonstrated that the building components are able to store heat dynamically by intelligent prediction of occupancy, outside weather, and prices. With an autonomous auction platform, Demand-side Management activities can be targeted to buildings which are most suitable to shift demand. The order of building participation is determined by individual consumer comfort and thermal dissipation. As predictive Demand-side Management relies on dynamic pricing and engagement of District Heating and Cooling customers and producers, the thesis proposes a concept to achieve a win-win situation for these stakeholders. In order to ensure a reasonable allocation of benefits from Demand-side Management and provide a more accurate demand prediction, new business models could emerge. These models can challenge producers and customers to revalue District Heating and Cooling.Nykyaikaiset teknologiat mahdollistavat rakennuksen energiankulutuksen hallinnan tavalla, joka minimoi kustannuksia ja maksimoi kuluttajien mukavuutta. Kustannusten alentaminen on kaukolämmössä ja -jäähdytyksessä perinteisesti saavutettu energiansäästöllä. Kuluttajien mukavuutta on taas parannettu tasaisemmalla lämmönjakelulla. Viimeisten vuosien aikana tutkijat ovat esittäneet kysyntäjoustoa ketteränä tapana alentaa systeemitason kustannuksia. Kysyntäjousto pykii siirtämään ajallisesti osan tehon huippukuormista. Energiankulutusta ei välttämättä vähennetä. Tämä työ tarjoaa mallin, joka hyödyntää kaukolämmitteisten rakennusten lämpökapasiteettia energiavarastoina. Mallia varten on kehitetty kaukolämpöjärjestelmä, jonka antamat hintasignaalit perustuvat muuttuviin tuotantokustannuksiin. Näitä signaaleja hyödynnettiin kahdessa simulaatiossa. Ensimmäinen optimoi kuormia systeemitasolla siirtäen olemassa olevia kulutusprofiileja, kun taas toinen simulaatio käsitteli ihanteellista huonemallia. Molempien simulaatioiden tarkoituksena on varastoida lämpöä etukäteen rakenteisiin ja purkaa sitä hintapiikkien aikana. Toisin kuin aiemmissa tutkimuksissa, lämmönsäätimet reagoivat muuttuviin hintasignaaleihin. Tällä tavalla rakennukset kuluttivat lämmitysenergiaa käyttäjien yksilöllisten kulutusprofiilien mukaisesti, ja kysyntäjoustotoimenpiteisiin ryhdyttiin, kun koko järjestelmä sitä vaati. Simulointimalli osoitti, että ennustava kysyntäjousto voi alentaa rakennuksen lämmityskustannuksia 4% lämmityskauden aikana. Suurimmat kustannussäästöt koituvat energiantuottajille, sillä muuttuvat tuotantokustannukset laskivat simulaatiossa 6% käyttäen 15% rakennuskannan pinta-alasta hyödyksi. Huonesimulaatio osoitti, että rakennuksiin voi varastoida dynaamisesti lämpöä läsnäolon, sään ja hintojen älykkäällä ennustamisella. Itsenäisellä huutokauppa-alustalla kysyntäjouston toimintaa voidaan kohdistaa rakennuksiin, joilla on parhaimmat edellytykset siirtää hetkittäin lämmitystehoa. Tämä jako määräytyy kuluttajien mieltymysten ja rakennuksen lämpöhäviöiden mukaan. Koska ennakoivan kysyntäjouston täyden potentiaalin hyödyntäminen perustuu asiakkaiden sekä tuottajien sitoumukseen, tutkielma ehdottaa konseptia, jossa kaikki osapuolet hyötyvät kysyntäjoustosta. Tutkielmassa käy ilmi, että uusia liiketoimintamalleja voi syntyä varmistamaan kohtuullisen hyödynjaon ja parantamaan lämpökuormien ennustettavuutta. Nämä mallit voivat haastaa osapuolia löytämään uutta arvoa kaukolämmöstä ja -jäähdytyksestä