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

Innovative Technologies for District Heating and Cooling: InDeal Project

The paper discusses the outcomes of the conference organized by the InDeal project. The conference took place on 12 December 2018 in Montpellier as part of the EnerGaia energy forum 2018. A holistic interdisciplinary approach for district heating and cooling (DHC) networks is presented that integrates heterogeneous innovative technologies from various scientific sectors. The solution is based on a multi-layer control and modelling framework that has been designed to minimize the total plant production costs and optimize heating/cooling distribution. Artificial intelligence tools are employed to model uncertainties associated with weather and energy demand forecasts, as well as quantify the energy storage capacity. Smart metering devices are utilized to collect information about all the crucial heat substations’ parameters, whereas a web-based platform offers a unique user environment for network operators. Three new technologies have been further developed to improve the efficiency of pipe design of DHC systems: (i) A new sustainable insulation material for reducing heat losses, (ii) a new quick-fit joint for an easy installation, and (iii) a new coating for reducing pressure head losses. The results of a study on the development and optimization of two energy harvesting systems are also provided. The assessment of the environmental, economic and social impact of the proposed holistic approach is performed through a life cycle analysis. The validation methodology of the integrated solution is also described, whereas conclusions and future work are finally given

The Status of Research and Innovation on Heating and Cooling Networks as Smart Energy Systems within Horizon 2020

The European Union is funding scientific research through the Horizon 2020 Framework Programme. Since the key priorities for the next few decades are the reduction in carbon emissions and the enhancement of energy system conversion efficiency, a collection of the most recent research projects can be beneficial to researchers and stakeholders who want to easily access and identify recent innovation in the energy sector. This paper proposes an overview of the Horizon 2020 projects on smart distributed energy systems, with particular focus on heating and cooling networks and their efficient management and control. The characteristics of the selected projects are summarized, and the relevant features, including the energy vectors involved, main applications and expected outputs are reported and analyzed. The resulting framework fosters the deployment of digital technologies and software platforms to achieve smart and optimized energy systems

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

Best practices and informal guidance on how to implement the Comprehensive Assessment at Member State level

This report details a methodology for performing a cost-benefit analysis (CBA) identifying the most resource and cost-efficient solutions to meet heating and cooling demands for a given country or region in accordance with Article 14(3) and taking in account Part 1 of Annex IX of the of the Energy Efficiency Directive (EED) (EC, 2012). The methodology includes guidelines how to: (1) collect data about energy consumption and supply points needed to construct heat maps, (2) how to identify system boundaries, (3) assess the technical potential that can be satisfied by efficient technical solutions, including high efficiency cogeneration, micro-cogeneration and efficient district-heating and cooling. (4) define baseline and alternative scenarios, including quantifying the cost and benefits of both scenarios. This comprises for example the economic value of other effects is estimated, mainly, the changes in socio-economic and environmental factors. Cost-Benefit Analyses integrate all costs and benefits over a long period are integrated in a unique estimate, the Net Present Value, which provides information about the net change of welfare derived from the implementation of the different heating and cooling scenarios. In the end, the cost-benefit analyses shall provide information about which are the most cost-efficient solutions to meet the heating and cooling needs of a country or a region.JRC.F.6-Energy Technology Policy Outloo