Demand side management in district heating systems

ABSTRACT This paper describes a multiagent system that has made the voyage from research project to commercialised product. The purpose for the multiagent system is to dynamically control a system so that the load of the system is below certain threshold values without reduction of quality of service and by that, to avoid the usage of top load production sources and to reduce energy consumption. The fundamental idea behind the system is that a large number of small local decisions taken all in all have great impact on the overall system performance. A field-test as well as a return of investment analysis are presented

Demand side management in district heating systems by innovative control

Demand side management can be successfully applied to district heating systems for shaving thermal peaks. Peak shaving allows increasing share of convenient and less pollutant sources (waste heat, cogeneration and renewables) and enabling further building connections without modifying the pipelines. Demand side management in district heating is mainly done by shifting the load. Another interesting option consists in adjusting the substation regulation strategy; this approach not affects the heating schedule. This paper aims at analysing the opportunities for peak shaving using an innovative regulation strategy in the district heating substations, by controlling with a building model the effects on the indoor comfort conditions. The regulation strategy adopted is the Differential of Return Temperatures (DRT), that includes a constraint on the cold outlet section of the heat exchanger. This paper shows that thermal peak of building demand reducing on average of 15% by using the DRT regulation. Considering an entire distribution network, taking into account its thermal dynamics, the total peak request can be shaved of about 24%. Setting of the DRT regulation strategy has been shown being crucial for achieving satisfying peak shaving without compromising the indoor comfort conditions

Integrated model concept for district energy management optimisation platforms

District heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.The research activities leading to the described developments and results, were funded by the European Uniońs Horizon 2020 MOEEBIUS project, under grant agreement No 680517. Authors would like to ex-press their gratitude to the operator of the Vozdovac district heating system (Beogradske elektrane) for the specifications used to develop and calibrate the models, and to Solintel M&P, SL for developing the initial versions of the EnergyPlus models (including only the geometrical and constructive definition of the buildings), in the framework of the MOEEBIUS project

Energy Services in Sweden - Customer Relations towards Increased Sustainability

Energy use and supply are evident issues to consider for a sustainable development, where the economic, social and environmental aspects are all important. In large grid-bound systems, the supply of energy is usually a rather invisible activity and the contacts between household custo-mers and utilities are sometimes only repre-sented through the energy bill. In this thesis, three particular fields are emphasized where these interactions comes into focus: Electricity peak load problems and load manage-ment in households; Energy monitoring and feedback, and; The selling of district heating to households in de-tached house areas. Improved customer relations in these areas can both increase the energy utilities abilities to compete on the markets and to contri-bute to an increased sustainable development within the energy sector. The traditional ways to handle peak load problems in the Swedish electricity system have been to build new power plants and to reinforce the elec-tricity grid. However,there are many reasons why solutions should be sought for on the demand-side. This thesis discusses the issues of load management through technical load control of households’ electric heating systems and electric water heaters, and through indirect load management with different pricing of electricity.The new Swedish law about monthly accurate billing of electricity for household customers has influenced the electricity network owners to install new automatic meter reading (AMR) systems. Hourly metering can give raise to a new set of data about household electricity use, that can be utilised to provide detailed characteristics of load demand and consumption patterns and serve as a basis for customer segmentation. This information can be useful when developing new energy services, new pricing of electricity, new load management strategies and demand response programs. In this thesis, customer preferences towards feedback on electricity use and different types of billing are investigated and the results from this research can make a contribution to the knowledge of customers’ need and awareness of different kinds of feedback. Conversion of electric heating systems in detached houses to district heating can contribute to solve peak load problems, to lower emissions in the energy system and may constitute a new heat demand needed for new introduction of Combined Heat and Power (CHP) plants. Effective market strategies are important in order to achieve a high connection rate of household customers to the district-heating grid. Results from two studies in this thesis can contribute to the knowledge of customer preferences, attitudes and decision-making processes that play an important role for the development of more effective marketing strategies for district heating

Treatment and analysis of smart energy meter data from a cluster of buildings connected to district heating:A Danish case

District heating has been found to be a key component of future and reliable smart energy grids comprising 100% of renewable energy sources for countries with dominant heating season. However, these systems face challenges that require a deeper understanding of the coupling between the distribution networks and the connected buildings, to enable demand-side management and balance the intermittence of renewables. In recent years, many smart energy meters have been installed on the heating systems of Danish dwellings connected to district heating, and the first yearly measurement data sets of large building clusters are now available. This article presents the methodology for the pre-processing and cluster analysis (K-means clustering) of a one-year-long smart energy meter measurement data from 1665 Danish dwellings connected to district heating. The aim is to identify typical household daily profiles of heat energy use, return temperature, and temperature difference between the supply and the return fluid. The study is performed with the free software environment “R”, which enables the rapid extraction of information to be shared with professionals of the building and energy sectors. After presenting the preliminary results of the clustering analysis, the article closes with the future work to be conducted on this study case

Impact of network modelling in the analysis of district heating systems

Network modelling is crucial for the simulation of district heating system responses to changes in operating conditions. Various applications, aimed at finding optimal district heating design and operations, neglect or strongly simplify the network dynamics. In this paper, the effect of including network dynamics in district heating system modelling has been analyzed. Different physical contributions have been considered separately: thermal losses, thermal transients and delay time due to the various costumer distances. This allows estimating the significance of the various phenomena in the estimation of the thermal request, in particular during demand peaks. Results shows that the thermal power required by the thermal plant is significantly different if evaluated relying on a network model or not; in case of thermal peak this is under-estimated up to 20% if the network dynamic is not taken into account. In particular, the inclusion of the thermal transient in the model is found to be crucial for considerably improving the result accuracy in the peak estimation. Effects for inclusion of thermal losses calculation have been quantified; errors reaches 4% in case of not perfectly insulated pipelines. The effect of neglecting network dynamics have also been analyzed in the context of demand side management (DSM) district heating systems. In particular, the effects are tested on a model for the best rescheduling of on-off time of the building heating device to optimally shave the thermal peak. Results show that the benefits achieved by the demand response model that include the thermal dynamics contribution increase from 1 to 18%; this is because the contribution of the different times the water trains takes to reach the plants (from the buildings) and of the water in the pipelines cooled down during night are relevant. Furthermore, different options are discussed to take into account compactly the network dynamic

Implementing Optimal Operation of Multi-Energy Districts with Thermal Demand Response

The combination of different energy vectors in the context of multi-energy systems is a crucial opportunity to reach CO2 reduction goals. In the case of urban areas, multi-energy districts can be connected with district heating networks to efficiently supply heat to the buildings. In this framework, the inclusion of the thermal demand response allows for significantly improve the performance of multi-energy districts by smartly modifying the heat loads. Operation optimization of such systems provides excellent results but requires significant computational efforts. In this work, a novel approach is proposed for the fast optimization of multi-energy district operations, enabling real-time demand response strategies. A 3-step optimization method based on mixed integer linear programming is proposed aimed at minimizing the cost operation of multi-energy districts. The approach is applied to a test case characterized by strongly unsteady heat/electricity and cooling demands. Results show that (a) the total operation cost of a multi-energy district can be reduced by order of 3% with respect to optimized operation without demand side management; (b) with respect to a full optimization approach, the computational cost decreases from 45 min to 1 s, while the accuracy reduces from 3.6% to 3.0%

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

Solar-thermal and hybrid photovoltaic-thermal systems for renewable heating

Grantham Briefing Papers analyse climate change and environmental research linked to work at Imperial College London, setting it in the context of national and international policy and the future research agenda. This paper and other Grantham publications are available from: www.imperial.ac.uk/grantham/publicationsThis paper looks at the barriers and opportunities for the mass deployment of solar-thermal technologies and offers a vision for the future of solar-thermal systems. HEADLINES: -Heat constitutes about half of total global energy demand. Solar heat offers key advantages over other renewable sources for meeting this demand through distributed, integrated systems. -Solar heat is a mature sustainable energy technology capable of mass deployment. There is significant scope for increasing the installed solar heat capacity in Europe. -Only a few European countries are close to reaching the EU target of 1 m2 of solar-thermal installations per person. -One key challenge for the further development of the solar-thermal market arises from issues related to the intermittency of the solar resource, and the requirement for storage and/or backup systems. The former increases investment costs and limits adaptability. -An analysis of EU countries with good market development, suggests that obligation schemes are the best policy option for maximising installations. These do not present a direct cost to the public budget, and determine the growth of the local industry in the long term. -Solar-thermal collectors can be combined with photovoltaic (PV) modules to produce hybrid PV-thermal (PV-T) collectors. These can deliver both heat and electricity simultaneously from the same installed area and at a higher overall efficiency compared to individual solar-thermal and PV panels installed separately. --Hybrid PV-T technology provides a particularly promising solution when roof space is limited or when heat and electricity are required at the same time.Preprin