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Modelling District Heating in a Renewable Electricity System
Abstract
With the decarbonisation of electricity generation, large scale heat pumps are becoming increasingly viable for district heating combined with thermal energy storage, district heating can provide flexibility to the electricity grid by decoupling demand from supply. This thesis examines how district heating with heat pumps and thermal energy storage can integrate with and provide a benefit to an electricity system with predominantly renewable generation. The scope of work comprises three interlinked models underpinned by the same set of meteorology data, fundamentally coupling supply and demand. First, heat load data are surveyed, and an hourly demand profile is simulated. Disaggregation of district heating loads from the national demand is accomplished via segmentation of the building stock to model heat demand at high spatiotemporal resolution. Second, a novel method of pricing hourly electricity in a zero carbon, capital-intensive renewable system with electricity storage is developed and applied to a dispatch simulation to generate hourly electricity prices. Third, a dynamic model of district heating is constructed to simulate the meeting of heat loads with different design configurations using electricity as the energy source. Model predictive control is applied with varying forecast horizons so as to minimise the cost of electricity to meet the heat demand given a time series of hourly prices and consequently optimising the capacity of thermal energy storage. It was found that a thermal energy storage capacity equivalent to 1.3% of annual demand is sufficient to minimise operating costs. Finally, the potential impact of district heating on balancing the electricity system is analysed and an equivalence between thermal and electric storage is examined. While this is highly dependent on annual conditions, it can be as much as 3.5 units of thermal storage for every unit of electrical grid storage on the system. This could potentially reduce the investment in grid storage by £36 billion, underlining the significant financial benefits of thermal storage to the whole system. The research highlights the important potential of district heating to the UK’s energy system strategy.
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Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even though much research and development has been performed in the field, there are several pitfalls and challenges towards dynamic district heating and cooling simulation for everyday use. This article presents the experiences from developing and working with a city-scale simulator of a district heating grid located in Luleå, Sweden. The grid model in the case study is a physics based white-box model, while consumer models are either data-driven black-box or gray-box models. The control system and operator models replicate the manual and automatic operation of the combined heat and power plant. Using the functional mock-up interface standard, a co-simulation environment integrates all the models. Further, the validation of the simulator is discussed. Lessons learned from the project are presented along with future research directions, corresponding to identified gaps and challenges.
This paper presents an estimation of the European (EU28) industrial heat pump market potential in terms of magnitude, sizing and number of units. This study is carried out in order to provide technology suppliers and manufacturers of industrial heat pumps perspectives for the technology. Potential heat pump applications in the food, paper, chemical and refining sectors are identified considering a maximum sink temperature of 200°C. This is achieved utilising a bottom-up methodology that uses detailed information from individual processes in the aforementioned sectors. Combining individual process data with typical plant capacities provides information on the heating capacities of heat pumps. The data is upscaled to European level, using production statistics relevant to the individual processes analysed. Since the database of processes is generic in nature and not fully covering the whole industrial sector, the results of this analysis provide a conservative estimate of the heat pump market potential. The results show a potential cumulative heating capacity of industrial heat pumps in EU28 of 23.0 GW, consisting of 4174 heat pump units which are able to cover 641 PJ/a of process heat demand. The largest number of heat pump units (%) can be found for heating capacities <10 MW, making up about 50% of the total market cumulative heating capacity. Clearly, there is a large market ahead for industrial heat pump manufacturers and suppliers.
Large scale storage of heat is critical for the successful decarbonisation of the UK’s energy mix and for grid-balancing. Heat generation currently accounts for 50% of all energy use in the UK and most of this is produced by burning fossil natural gas. Heat is regarded as a single-use commodity, discarded or dissipated when not required in summer yet a lifesaving necessity during the colder winter months. Here we estimate the theoretical potential capacities for the storage of heat in the subsurface using aquifers and flooded mines, with a consideration of seasonal storage of heat in particular. We set this against the theoretical potential volumes of waste heat and solar thermal energy that could be exploited. This contributes to the wider knowledge base of the capacity of different forms of energy storage available through other means and highlights the potential for the UK.
Our calculations indicate that the theoretical potential for large-scale underground thermal-energy storage in the UK is substantial, much larger than which might ever be needed and the location of such storage is well matched to the places where people live and work and therefore where the demand for heat occurs.
Decarbonisation of the electricity system requires significant and continued investment in low-carbon energy sources and electrification of the heat and transport sectors. With diminishing output and shorter operating hours of conventional large-scale fossil fuel generators, there is a growing need and opportunity for other emerging technologies to provide flexibility in the context of grid support, balancing, security services, and investment options to support a cost-effective transition to a lower-carbon energy system. This article summarises the key findings from a range of studies investigating the potential benefits and challenges associated with the future low-carbon energy system. The key challenges associated with balancing local, national and regional objectives to minimise the overall cost of decarbonising the future energy system are also discussed. Furthermore, the paper highlights the importance of cross-energy vector flexibility, and coordination across electricity, heat, and gas systems which is critical for shaping the future low-carbon energy systems. Although most of the case studies presented in this article are based on the UK, and to some extent the EU decarbonisation pathways, the overall conclusions regarding the value of flexibility are relevant for the global energy transition.
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.
The paper quantifies the benefits of low return temperature to reduce both the capital and operating cost of district heating (DH) schemes. The key benefits are reduction in network heat losses, smaller pipe sizing and improved boiler and CHP efficiency. The paper uses energy centre and HIU data from a number of operating DH schemes in London that serve newly built flats. The data presented includes flow duration curves that indicate the long duration of periods with very low load and return temperature data that shows the impact of incomplete commissioning. The paper discusses how designs can be improved to lower the DH return temperature at all operating conditions. The issues considered include, impact of over estimating peak load, impact of additional heat exchangers in system, pump selection and control, HIU and secondary system selection.
A brief history is provided of models of energy use in the UK building stock, with the focus on the non-domestic sector. This history leads to an account of the development, since 2009, of the 3DStock method for modelling complete building stocks, both domestic and non-domestic. The paper explains how 3DStock models are built and the data sources used. Special emphasis is placed on the relationship of premises (the floorspace occupied by organisations) to buildings. Energy use may be metered at the level of premises, buildings or groups of buildings. Representing the patterns in which premises relate to buildings is therefore crucial to the modelling process, and in particular to the precise measurement of energy intensities. Applications of 3DStock models in building science and policy tools are reviewed, including the London Building Stock Model (LBSM), delivered to the Greater London Authority (GLA) in 2020. This ‘digital twin’ can be used for the monitoring, simulation and analysis of the building stock. Implications for research and policy are discussed, particularly for energy epidemiology, density, high-rise buildings, retrofit potential, energy-use intensity and benchmarking. Data are in place to extend 3DStock modelling to the whole of England and Wales. / Policy and practice relevance: The models can be used to assess energy demand in large numbers of buildings in relation to a range of variables, including built form, age, construction and activities. Poorly performing buildings can be identified, and the potential for retrofit—including renewable technology installations—evaluated using the same variables plus information contained in Energy Performance Certificates (EPCs). Policies for addressing fuel poverty can be evaluated by making links to confidential socioeconomic data on occupants. In conjunction with a dynamic building energy-simulation tool, scenarios can be investigated for retrofit, the potential for renewables and issues in demand-side management. Additional uses are in the precise measurements of density and of the three-dimensional character of the urban fabric. There could be applications in public health, the modelling of indoor and outdoor air pollution, and the tracking of material flows. 3DStock models might also be integrated with digital twins of infrastructure systems and networks.
With electric heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the heat demand and the coefficient of performance (COP) of heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9878849
The transition of district heating and electrical distribution grids from traditionally independent to actively coupled and operated networks is seen as an important step on the way to smart energy networks. This work presents a method that enables a detailed technical assessment of the operation of such coupled heat and power networks. It is based on a sequential coupling approach of a dynamic thermalhydraulic model for the district heating network and a quasi-static model for the electrical distribution network. Different use cases are highlighted where a local coupling of the networks with power-to-heat is supporting the transition to smart energy networks, i.e., lowering district heating supply temperatures, accommodating renewable energy sources in the power network and integrating low-temperature heat sources into the district heating network. All three use cases are implemented in example applications to showcase the versatility of the method. The results underline the presented method's ability to perform detailed technical assessments of coupled heat and power networks.
A data-driven approach is proposed to discover heat load patterns in district heating. • The first large-scale analysis of all the buildings in six different categories is presented. • We showcase how typical and atypical behaviors look like in the entire network in Sweden. • The results show that our method has a high potential to be deployed and used in practice. A R T I C L E I N F O Keywords: District heating Energy efficiency Heat load patterns Clustering Abnormal heat use A B S T R A C T Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers' heat-use habits.
Long-term forecasts of the aggregate electric load profile are crucial for grid investment decisions and energy system planning. With current developments in energy efficiency of new and renovated buildings, and the coupling of heating and electricity demand through heat pumps, the long-term load forecast cannot be based on its historic pattern anymore. This paper presents part of an on-going work aimed at improving forecasts of the electric load profile on a national level, based on a bottom-up approach. The proposed methodology allows to account for energy efficiency measures of buildings and introduction of heat pumps on the aggregated electric load profile. Based on monitored data from over 100 non-residential buildings from all over Norway, with hourly resolution, this paper presents panel data regression models for heat load and electric specific load separately. This distinction is crucial since it allows to consider future energy efficiency measures and substitution of heating technologies. The data set is divided into 7 building types, with two variants: regular and energy efficient. The load is dependent on hour of the day, outer temperature and type of day, such as weekday and weekend. The resulting parameter estimates characterize the energy signature for each building type and variant, normalized per floor area unit (m²). Hence, it is possible to generate load profiles for typical days, weeks and years, and make aggregated load forecasts for a given area, needing only outdoor temperature and floor areas as additional data inputs.
Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.
Around 80% of domestic heat demand in Great Britain (GB) is supplied by natural gas, but continuing to heat dwellings in this way is unlikely to be compatible with national emission reduction targets. Electrical heating using heat pumps is expected to play a significant role in future space heating and hot water provision. The assessment of future heating technologies requires knowledge of the current demand for heat at short time intervals in order to evaluate peak demands and possible storage requirements. Existing half-hourly national heat demand estimates are built on data from small samples of dwellings. This paper provides estimates of GB domestic heat demand under mild, normal and cold weather conditions based on data from over 6000 dwellings collected between May 2009 and July 2010 that participated in the GB smart meter trial. The calculated peak domestic heat demand of 170 GW is around 40% lower than previously calculated suggesting that the difficulties surrounding the electrification of heat are far less profound than previously assumed. These results can be used in the development of future energy pathways and scenarios.
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.
District heating uptake has grown with the increasing need for cleaner and more efficient energy supply. This has resulted in a rising number of new developments signing up to a district heating scheme, typically powered by Combined Heat and Power (CHP) boilers or biomass boilers with supplemental electrical or gas grid connections. These schemes have advanced rapidly in recent years, with much of the research focus targeting lower carbon technologies, improved load prediction and peak demand management. We assess the current status of District Heating Networks (DHNs) in the United Kingdom using published case studies and suggest next steps to improvement. Our findings show that the United Kingdom has good potential for uptake of district energy given the current political climate and government incentives, however significant improvements must be made to further penetrate the heating market.
Localisation of energy technologies and policies is increasing the need for high-resolution spatial and temporal energy demand simulation modelling, which goes beyond annual and national scale. Increasing the temporal resolution is crucial for demand side management modelling or for the simulation of load profile changes due to the installation of new technologies such as heat pumps. Increasing the spatial resolution enables regional energy planning and capturing the spatial dynamics of drivers of energy demand. Yet regional and local energy grids are interconnected with national and continental networks, so to capture multi-scale effects, high resolution is required everywhere. A high-resolution bottom-up engineering energy demand simulation model is introduced, which projects energy demands both for a high spatial and high temporal scale and enables spatial explicit simulation of model parameters. The model is applied for exploring implications of the electrification of heat by a large-scale uptake of heat pumps for water and space heating in the United Kingdom and to simulate heat pump related demand side management opportunities. We simulate a change in peak electricity heating load of −0.4 – 21.5 GW for 50% heat pump uptake for space heating demands across different scenarios resulting in an increase of total peak electricity demand of 3.3–31.2 GW (6.3–59.8%). The simulation results show considerable regional differences in change of electricity load factors (−17.2–8.4%) and peak electricity demands (−9.9–206.1%). The potential to reduce national electricity peak load with managed heat pump load profiles for heating is simulated to be 0.2–5.8 GW (0.4–11.1%). These results exemplify the importance of discussing heat-pump induced change in peak electricity demands within a scenario context. Including different drivers in energy demands and their variability considerably affects the scale of anticipated electricity peak demand.
The UK has set ambitious targets to reduce carbon emissions, improve energy efficiency and affordability, encourage renewable energy generation, and reduce dependency on imported fossil fuels. Heating is the most essential component of the UK’s current residential energy consumption, and it is mostly supplied through the direct burning of natural gas. With constantly changing market conditions and political regulatory frameworks, technology assessments and cost-effective planning strategies are critical for long-term energy and environmental policy designing. Electric heat pumps with decarbonised electricity are proposed as promising technologies that could replace gas heating and contribute to the UK’s future low-carbon heat mix. District heating has been transforming over generations in order to better utilise renewables or resources that otherwise would be wasted. Both technologies have been well developed, with abundant scientific research and industrial experiences in some European countries over the past few decades. However, the market shares of heat pumps and district heating networks are low in the UK. This paper explores empirical heat consumption from smart meter data in different types of dwellings in the UK and the role of heat pumps and district heating for different types of dwellings on different scales. This study investigates heat pumps in individual households versus district heating networks through a levelised cost model, to present their comparative environmental and economic advantages. This study shows that economies of scale arise in the UK’s district heating networks with large heat pumps, but the costs of heat are significantly higher than individual gas boilers.
District heating risks to lose competitiveness the lower the linear heat density of a district is. The distribution network needs to be highly efficient in order to ensure economic feasibility. The heat distribution temperatures are crucial to keep distribution heat losses as low as possible. For a new development in Germany consisting mainly of single family houses, two district heating networks at different supply temperature levels are examined in terms of economic and efficiency aspects. Depending on the required temperature level and temperature difference between supply and return the economics of the heat supply system change. The required pipe diameters are affected, supplementary system components are needed due to lower supply temperatures etc. This study analysed the impact of design temperature and operating strategy on the economic feasibility of the distribution infrastructure, the district heating network and shows the impact on system costs. The total heat generation costs are separated in costs originating from the central heat supply unit, the distribution infrastructure, and the decentralized heat supply units and system engineering. The analysis discusses how the system design temperature effects the fix costs and variable costs of new heat supply systems based on (ultra-)low-temperature district heating. Added to this, an exemplary seasonal strategy was investigated, which provides a switch-off of the network during low load summer period, avoiding heat distribution losses. This study demonstrates that ultra-low-temperature district heating ensures important improvement of heat distribution efficiency, favorable conditions for renewable heat integration while showing no economic disadvantage compared to low-temperature district heating.
Despite the advancements in thermal energy storage (TES) systems for combined heat and power plants (CHP) operation, these technologies are not implemented in CHP-based district heating (DH) systems in some of the countries where DH systems are still in development. An analysis of economic and environmental benefits that can be reaped as a result of the TES integration within the DH system is required to successfully promote TES as a technology for the DH system improvement. An actual large-scale biomass CHP-based DH system is explored as a case study. Scenarios for two types of support policies (feed-in premium electricity produced by biomass CHP and high-efficiency biomass CHP) were examined by varying the size of TES. The aim of the research was to evaluate the impact of the biomass CHP support policy on TES integration feasibility. Calculations show that TES installation combined with biomass CHP is less feasible when all of the electricity produced by biomass CHP is subsidised.
Backcasting as a scholarly and planning approach is increasingly used in futures studies in fields related to urban sustainability as an alternative to traditional planning approaches and a formal element of future strategic initiatives. It is viewed as a natural step in operationalizing sustainable development within different societal spheres. As a holistic urban development strategy, smart sustainable cities represent a manifestation of sustainable development as a process of change and a strategic approach to achieving the long–term goals of sustainability. Achieving smart sustainable cities represents an instance of urban sustainability, a concept that refers to a desired state in which a city strives to retain the balance of socio–ecological system through sustainable development as a desired trajectory. This long–term goal requires fostering linkages between scientific and social research, technological innovations, institutional practices, and policy design and planning in relevance to urban sustainability. It also requires a long–term vision, a trans–disciplinary approach, and a system–oriented perspective on addressing environmental, economic, and social issues. These requirements are at the core of backcasting as an approach to futures studies. Backcasting is a special kind of scenario methodology to develop future models for smart sustainable city as a planning tool for urban sustainability. Goal–oriented backcasting approaches declare long–range targets that lie quite far in the future. Visionary images of a long–term future can stimulate an accelerated movement towards achieving the goals of urban sustainability. The backcasting approach is found to be well–suited for long–term urban sustainability solutions due to its normative, goal–oriented, and problem–solving character. Also, it is particularly useful when: dealing with complex problems and transitions, the current trends are part of the problem, and different directions of development can be allowed given the wide scope and long time horizon considered. A number of recent futures studies using backcasting have underlined the efficacy of this scholarly and planning approach in terms of indicating policy pathway for sustainability transitions and thus supporting policy makers and facilitating and guiding their actions. However, as there are a number of backcasting approaches used in different domains, and the backcasting framework is adaptive and contextual in nature, it is deemed highly relevant and useful to devise a scholarly and planning approach to strategic smart sustainable city development. This paper has a fourfold purpose. It aims to (1) provide a comparative account of the most commonly applied approaches in futures studies dealing with technology and sustainability (forecasting and backcasting); (2) to review the existing backcasting methodologies and discuss the relevance of their use in terms of their steps and guiding questions in analyzing strategic smart sustainable city development as an area that is at the intersection of city development, sustainable development, and technology development; (3) to synthesize a backcasting approach based on the outcome of the review and discussion, and (4) to examine backcasting as a scholarly methodology and planning approach by looking at its use in the Gothenburg 2050 Project, as well as to use this case to illustrate the core of the synthesized approach. The synthesized scholarly and planning approach serves to help researchers and scholars in analyzing strategic smart sustainable city development to assist planners, policy makers, and decision makers in their endeavor to implement smart sustainable cities.
Increasing penetrations of variable renewable energy (VRE) can affect wholesale electricity price patterns and make them meaningfully different from past, traditional price patterns. Many long-lasting decisions for supply- and demand-side electricity infrastructure and programs are based on historical observations or assume a business-as-usual future with low shares of VRE. Our motivating question is whether certain electric-sector decisions that are made based on assumptions reflecting low VRE levels will still achieve their intended objective in a high VRE future. We qualitatively describe how various decisions may change with higher shares of VRE and outline an analytical framework for quantitatively evaluating the impacts of VRE on long-lasting decisions.
We then present results from detailed electricity market simulations with capacity expansion and unit commitment models for multiple regions of the U.S. for low and high VRE futures. We find a general decrease in average annual hourly wholesale energy prices with more VRE penetration, increased price volatility and frequency of very low-priced hours, and changing diurnal price patterns. Ancillary service prices rise substantially and peak net-load hours with high capacity value are shifted increasingly into the evening, particularly for high solar futures.
While in this report we only highlight qualitatively the possible impact of these altered price patterns on other demand- and supply-side electric sector decisions, the core set of electricity market prices derived here provides a foundation for later planned quantitative evaluations of these decisions in low and high VRE futures.
The Nordic power market has exceptionally low carbon emissions. Energy efficient combined heat and power (CHP) plays an important role in the market, and also produces a large share of Nordic district heating (DH) energy. In future Nordic energy systems, DH CHP is often seen as vital for flexibility in electricity production. However, CHP electricity production may not be profitable in the future Nordic market. Even currently, the prevailing trend is for CHP plants to be replaced with heat-only boilers in DH production. In this work, we aim to describe the future utilisation of CHP in the Nordic area. We use an electricity market simulation model to examine the development of the Nordic electricity market until 2030. We examine one main projection of electricity production capacity changes, and based on it we assess scenarios with different electricity demands and CO2 emission prices. Differences between scenarios are notable: For example, the stalling or increasing of electricity demand from the 2014 level can mean a difference of 15 e/MWh in the average market price of electricity in 2020. The results of this paper underline the importance of considering several alternative future paths of electricity production and consumption when designing new energy policies.
In Nordic countries, district heating accounts for a large share of the consumers’ heat demand. In Sweden, roughly 50% of the total heat demand is attributed to district heating. Which, over the past few years, is equivalent to around 50 TWh, and imposes a difficult balance between supply and demand for the suppliers of district heating. For large networks the propagation of heat from supplier to end-user can vary several hours. Further complexities of large networks, which can consist of multiple overlapping rings, is that during transient conditions the flow can actually change direction. A dynamic modeling library has been developed in Modelica using OpenModelica for district heating networks. Methods for modeling, handling data, simulating and the visualization of results has been developed using Matlab. The model has been validated using data from Mälarenergi AB, a local provider of district heating in Västerås, Sweden. The model provides to an acceptable degree in predicting the heat propagation and temperature distribution in a localized case study. Adding a higher level of robustness, the model has the capacity to handle bi-directional and reversing flows in complex ring structures. Through this work, the combination of OpenModelica and Matlab, a framework for automating the building and simulation of district heating networks is obtainable. The implications of automating network modeling from computer-aided design drawings allows for a quick robust overview of how the network is working and how prospective additions to the network could impact the end-users. Furthermore, incorporating visual aspects for heat propagation in a network contributes to a higher understanding of complex network structures.
Flexibility in district heating and cooling systems (thermal networks in general) is an important means to cope with the intermittent generation of heat and electricity as the share of renewable energy sources (RES) increases. An important source of flexibility is the thermal energy storage present in district heating and cooling networks, found in the thermal inertia of buildings, storage units and the network itself. To unlock this flexibility and to use it effectively and efficiently, a suitable control strategy is required. In this context, this paper presents a possible definition of flexibility and its sources in a thermal network. It reviews techniques to quantify flexibility and shows the need for a more advanced control strategy; moreover, it discusses the challenges involved in developing such a control strategy. Also, the literature on advanced control in thermal networks is reviewed, by making a distinction between central, distributed and hybrid control. Finally, possible future research topics are identified based on the findings.
Britain and Germany saw unprecedented growth of variable renewable energy (VRE) in the last decade. Many studies suggest this will significantly raise short-term power system operation costs for balancing and congestion management. We review the actual development of these costs, their allocation and policy implications in both countries. Since 2010, system operation costs have increased by 62% in Britain (with a five-fold increase in VRE capacity) and remained comparable in Germany (with capacity doubling). Within this, balancing costs stayed level in Britain (-4%) and decreased substantially in Germany (-72%), whilst congestion management costs have grown 74% in Britain and 14-fold in Germany. Curtailment costs vary widely from year to year, and should fall strongly when ongoing and planned grid upgrades are completed. Curtailment rates for wind farms have risen to 4-5% in Germany and 5-6% in Britain (0-1% for offshore and 15-16% for onshore Scottish farms). Policy debates regarding the balancing system are similar in both countries, focussing on strengthening imbalance price signals and the extent that VRE generators bear the integration costs they cause. Both countries can learn from each other's balancing market and imbalance settlement designs. Britain should reform its balancing markets to be more transparent, competitive and open to new providers (especially VRE generators). Shorter trading intervals and gate closure would both require and enable market participants (including VRE) to take more responsibility for balancing. Germany should consider a reserve energy market and move to marginal imbalance pricing.
In homes in the UK, it is very common to operate space heating intermittently; the heating is usually switched off when the occupants are asleep at night and when they are out during the day. The strong association between heating operation and household routines leads to a morning peak in demand which, if it persists following electrification of heating, will require significant reinforcement of electricity supply networks.
This paper examines factors that underpin how heating is used in the UK. A unique dataset of heating controller settings from 337 UK allows investigation of how patterns of heating operation in individual homes contribute to daily patterns of space heating energy consumption at the group level. A mixed method approach is followed, combining quantitative analysis of data with interviews with householders.
The concept of thermal routines is introduced, bringing a time dimension to the consideration of domestic thermal comfort and recognising that demand for space heating is linked to patterns of practices in the home, which are themselves linked to social routines, e.g. timing of work and school. The results from this study suggest that household thermal routines around 07:00 in the morning are a particularly important consideration for a transition to future energy systems with a high proportion of low carbon heat. Factors that currently limit flexibility of heating demand in the UK are identified, and the implications for a transition to low carbon heating sources are discussed.
UK electricity prices are the topic of lively debate, particularly industrial prices in the context of Brexit and the UK’s Industrial Strategy. This report was written as a contribution to that debate by Professor Michael Grubb and Paul Drummond, Senior Research Associate, at the UCL Institute of Sustainable Resources, with extensive and invaluable contributions on the Italian electricity system by our colleague Elsa Barazza. We benefited from international inputs by Felix Matthes (Öko-Institut, Berlin) and Davide Tabarelli at Nomismaenergia, Bologna and are most grateful to them for providing information and explaining the intricacies of their national electricity pricing systems. We would like to thank everyone who contributed their time and expertise, including through three workshops held to consider and debate the emerging findings. Their involvement was invaluable and some are listed below. However the findings and recommendations in this report cannot be attributed to any one individual or organisation, and the authors are responsible for the views expressed.
Fourth-generation district heating (4GDH) has been used as a label or expression since 2008 to describe a transition path for decarbonization of the district heating sector and was defined in more detail in 2014. During recent years, several papers have been published on a concept called fifth generation district heating and cooling (5GDHC). This article identifies differences and similarities between 4GDH and 5GDHC regarding aims and abilities. The analysis shows that these two are common not only in the overarching aim of decarbonization but that they also share to some extent the five essential abilities first defined for 4GDH. The main driver for 5GDHC has been a strong focus on combined heating and cooling, using a collective network close to ambient temperature levels as common heat source or sink for building-level heat pumps. It is found that the four 5GDHC network configurations are fundamentally different from ten other heat distribution configurations. Thus, 5GDHC can be regarded as a promising technology with its own merits, yet a complementary technology that may coexist in parallel with other 4GDH technologies. However, the term “generation” implies a chronological succession, and the label 5GDHC does not seem compatible with the established labels 1GDH to 4GDH.
This chapter provides an extensive survey on the modelling and optimisation of district heating systems (DHSs) focused on the heat distribution network, an energy analysis of unbalanced central heating systems, as well as a comprehensive discussion about pump control in heating stations, analysing the energy efficiency of flow control methods. For this purpose, the major components of a DHS have been described and discussed, and their modelling has briefly reviewed including numerical simulation models for heat sources, end-users, and especially, the distribution network. The main deterministic and heuristic optimisation techniques are briefly explained. A comparative energy analysis is performed on the hot water flow rate adjustment using throttling control valves and variable-speed drives in a district heating plant constructed in Romania. To correlate the pumped flow rate with the heat demand and to ensure the necessary pressure using minimum energy, an automatic device has been designed. Additionally, the performance of a fluid temperature control system using self-adjustable cables and the main plastic materials used for the manufacturing of tubes for heat distribution in buildings are presented, and a comparative analysis of the physical, mechanical, geometric and hydraulic characteristics of the tubes produced both from classical metallic materials and from different plastic materials is performed.
This paper presents a novel dynamic simulation tool able to assess and optimize the energy, environmental and economic performance of standard and innovative district heating/cooling systems. To this aim, several design and operating parameters (e.g.: weather conditions, heat selling price for users, national unitary price of electricity, etc.) are dynamically taken into account. The heating and cooling demands and loads of the buildings to be fed by the network working fluid are dynamically calculated. The system pipeline network is modelled through a suitable plug-flow approach. Fluid temperatures are calculated in each network node. The optimization of several system design and operating parameters is achieved for different objective functions. A suitable analysis is considered for selecting the most convenient urban zones for system application. The whole simulation model is implemented in a suitable computer code written in MatLab. By such tool useful design criteria and feasibility analyses can be obtained. To show the capabilities of the presented simulation tool, a novel case study, referred to a district heating system supplied by an existing thermoelectric power plant, was developed. The conducted analysis is based on system optimizations regarding the number of users, selling heat/electricity prices and system geometric features. As for example, for minimizing the system payback to about 14 year, the optimal number of users and network length are 5×103 and 2.7 km, respectively. In this case the primary energy savings and the avoided carbon dioxide emissions are about 11 GWh/y and 16.1 ktCO2/y, respectively.
The sizing of district energy systems involves a trade-off between reliability and continuity of service, and avoidance of capital and running costs associated with oversizing. Finding the most appropriate sizing requires a thorough understanding of energy demand. However, empirical data necessary to support such an understanding is scarce, and district energy systems are typically oversized. This study uses smart meter data from the largest field trial to analyse residential energy consumption in the UK. It presents graphically the seasonal and daily variations in energy consumption patterns, the weather dependence of energy loads, and peak hourly demand during particularly cold weather conditions. It also explores the diversity effect in residential energy consumption and computes the after diversity maximum demand at different levels of aggregations. Results show that, peak hourly gas consumption was nearly seven times higher than electricity consumption during the cold spells, while diversity reduced gas and electricity maximum demand per dwelling up to 33% and 47%, respectively. This empirical quantitative analysis of energy demand and diversity can support improved design and operation of district energy, and in particular, enable reduced capital and running costs, and an improved understanding of economies of scale for district heating networks.
Seasonal thermal energy storage (TES) is envisioned as a major player in the future district heating (DH) systems where large shares of renewables are being integrated. Therefore, in order to fulfill the seasonal tasks, such storage systems are characterized with large volumes. Yet, the integration of such large-scale storage technologies is not easily planned and realized. There exist numerous challenges e.g. TES type, volume and ground conditions, need to be tackled in order to obtain an optimal planning solution for TES integration. Given their promising applications, the scope of this work is limited to tank and pit thermal energy storage. Accordingly, this contribution firstly discusses the modeling of seasonal TES in finite element tools. Then, it examines the influence of a list of parameters i.e. TES construction type, geometry, volume and DH characteristics, on TES performance. Later, the work develops a methodology for construction techno-economic analysis of such technologies. It is revealed that the tank TES has always better performance than pit, but on the other hand it is always characterized with higher capital cost. As TES volume increases, the performance difference between tank and pit starts to vanish. Further, the DH characteristics play a major role in TES performance. It is depicted that lowering DH temperatures will ultimately lead to lower thermal losses from TES. Another important finding is the applicability of the suggested performance indicator for techno-economic analysis as it relates the technology capital cost to the effective volume of TES. The contribution also investigates the influence of insulation level on TES performance and it is found that for volumes larger than 500,000 m3, there is no major performance difference between the tank or the pit in case of insulation enclosing TES envelope. However, it is also revealed that insulation is needed only and solely to preserve the ground quality when large volumes are realized.
Given the importance of models in complicated problem solving, an inappropriate energy model can lead to inaccurate decisions and poor policy prescriptions. This paper aims at developing a decision support tool with which the selection of appropriate model characteristics can be facilitated for developing countries. Hence, it provides a comparative overview of different ways of energy models characterization and extracts the underlying relationships amongst them. Moreover, evolution of dynamic characteristics of energy models for developing countries is identified according to the previous studies on the developed and developing countries. To do this, it reviews the related literature and follows a systematic comparative approach to achieve its purposes. These findings are helpful in cases where model developers themselves are looking for appropriate characteristics in terms of certain purpose or situation.
Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%.
This paper reports a study on how hourly temperature variations of different heat sources influence the seasonal coefficient of performance (SCOP) of heat pumps (HPs) when supplying district heating. The considered heat sources were: groundwater, seawater, air and a combination of the three. The system included HPs, an electric peak load boiler and short-term storage. Linear programming was used to minimize annual electricity consumption of the system. This process also determined the optimum capacities of the HPs using different heat sources. The study was based on data for the area of Copenhagen, Denmark.
The results showed that the SCOP of seawater and air HPs, considering heat demand variations, was 11% and 15% lower, respectively, than their arithmetic mean performances. For a combination of heat sources, the optimum proportions of HP capacities were: 63%, 14% and 23% for the groundwater, seawater and air HP, respectively. The SCOP of such system was found to be 3%, 6% and 11% greater than the SCOP of a system using the heat sources individually. The results indicate that a maximum system performance may be achieved for HPs based on a combination of different heat sources.
Optimal operation of district heating (DH) systems usually relies on the forecast of thermal demand profiles of the connected buildings. Depending on the purpose of the analysis, thermal request can be required at various levels, from building level to thermal plant level. In the case of demand response for example, thermal request is necessary at a building level to evaluate its applicability and at a plant level to determine the effects. Thermal request profiles are quite different, depending on the observation point. Total requests are not just the summation of the downstream requests, mainly because of the thermal transients. The heat losses also contributes to modify the curves, although generally in a smaller way. In this work, a multi-level thermal request prediction is proposed. This approach has the aim of evaluating the thermal request in the various sections of DH network with reduced computational resources. This includes a compact model for the prediction of building demand and a network model in order to compose together the requests at the various levels. The application to a portion of the Turin district heating network is proposed. This shows that the network dynamics significantly affects the evolution, especially at peak load.
Variable renewable electricity sources have been shown to reduce wholesale electricity market prices. This is expected to reduce the incentive for investments in new electricity production capacity, and might even make these investments infeasible, if relying only on the income from trade on the current electricity market paradigms. In this paper, a novel approach for quantifying this effect in future energy systems is developed using a holistic energy system approach. The approach is applied to the case of Denmark in 2015, which is part of the Nordic and Baltic wholesale electricity market Nord Pool Spot. A holistic energy system model is created and verified according to both the Danish energy balance and the Nord Pool Spot system price in 2015. Using this verified model, the Nord Pool Spot system price is quantified at increasing amounts of onshore wind power, offshore wind power and photovoltaic in Denmark. It is found that regardless of which variable renewable electricity source is implemented, including a combination of the three, the Nord Pool Spot system price decreases as the amount of energy produced by these sources increases, and this effect occurs immediately as more is introduced.
In this paper we use stochastic polynomial optimization to derive high-performance operating strategies for heating networks with uncertain or variable demand. The heat flow in district heating networks can be regulated by varying the supply temperature, the mass flow rate, or both simultaneously, leading to different operating strategies. The task of choosing the set-points within each strategy that minimize the network losses for a range of demand conditions can be cast as a two-stage stochastic optimization problem with polynomial objective and polynomial constraints. We derive a generalized moment problem (GMP) equivalent to such a two-stage stochastic optimization problem, and describe a hierarchy of moment relaxations approximating the optimal solution of the GMP. Under various network design parameters, we use the method to compute (approximately) optimal strategies when one of, or both, the mass flow rate and supply temperature are varied for a benchmark heat network. We report that the performance of an optimally-parameterized fixed-temperature variable-mass-flow strategy can approach that of a fully variable strategy.
Modern, decentralised, multi-energy vector districts have great potential to reduce energy consumption and emissions. However, due to the complex nature of these systems, they require intelligent management to maximise their benefit. Therefore, this paper models the energy generation of a district heating plant for the purpose of hourly, operational optimisation. Crucially, non-linear, part-load efficiency curves, and minimum load percentages are included in the energy generation modelling as well as thermal energy storage. Due to the non-linearities, a genetic algorithm, optimisation approach was utilised. The optimisation framework was applied to a case study district with three distinct thermal energy generation sources, a gas CHP, gas boilers, and biomass boilers. The optimisation controlled the load percentage of each technology as well as varying thermal storage capacity to minimise the cost of meeting the heat demand. The study found that compared to the current, rule-based approach, the optimisation achieved a significant cost saving of 12.7% without any thermal storage. As the thermal storage capacity was increased the potential cost saving was also shown to increase proportionally to 22.6% with 1000 kWh of storage.
The aim of this document is to present the topic of operational optimization in District Heating (DH) systems, with special focus on different kinds of thermal energy storage. An optimization solution based on solving multiple Mixed Integer Linear Programming (MILP) problems has been proposed and implemented in the R programming environment.
The operational optimization in a DH system, especially if this system is supplied from a combined heat and power (CHP) plant, is a difficult and complicated task. Finding a global financial optimum requires considering long periods of time and including thermal energy storage possibilities into consideration. There are three important solutions for thermal energy storage: hot water tanks, utilization of thermal inertia of the network itself and utilization of thermal inertia of buildings. Each of these solutions has its advantages and disadvantages, and they can be combined to reach the maximum flexibility at lowest cost. However, modeling of operation with all of the thermal energy storage possibilities in place is a complicated task, since they influence the transient behavior of the network in different ways, and affect each other. On the other hand, optimal planning of heat production can be done only if simple and robust simulation models are available. Proposed solution allows simulation of three kinds of thermal energy storage, with their specific transient behaviors and interactions, at the same time keeping the model simple and ready to be used with a MILP solver. An iterative approach has been applied to non-linear phenomena, which allows solving a non-linear problem by multiple MILP optimizations. It has been successfully implemented in the “R” programming environment and tested on a simple example. The results can prove useful for DH system operators in the near future.
For the last decades energy efficiency initiatives have avoided enormous amounts of energy consumption, to the favor of the environment and consumer expenditures. Although there is still a big potential for further energy efficiency improvements it is time to move further and start preparing the whole energy system for the challenge of oversupply from intermittent renewable energy sources, particularly in the power sector. In 2015 the maximum one-hour power oversupply from wind turbines alone in Denmark happened the 26th of June, peaking at around 900 MW and the oversupply over a 15-hour period was 10 GWh. Known solutions to make use of oversupply in the power sector are power export, energy storage and halting the power generation. The power export needs to rely on sufficient capacity at local interconnectors, power demand in the importing country and there is an economic gain in the export. Energy storages can range from storing of power in batteries, pumped hydro, synthetic fuels to fuel displacing at other energy sectors. The last option is stopping the turbines, which should be avoided. The optimum energy storage would have large capacity, fast charging, high recovery efficiency and low cost. Scoring high on all criteria’s can be difficult when focusing on a single energy sector. By widening the perspective and start taking advantages of synergies between the energy sectors there is a possibility to score high on all three criteria’s. In this paper the potential of utilizing synergies between the power and heat sectors will be explored by considering the projections for the Danish energy system in 2025. The result of the analysis shows the optimum energy storage of renewable power is achieved through fuel displacement in the heating sector in combination with utility sized heat pumps, electric boilers and large thermal storages.
This review article presents a description of contemporary developments and findings related to the different elements needed in future 4th generation district heating systems (4GDH). Unlike the first three generations of district heating, the development of 4GDH involves meeting the challenge of more energy efficient buildings as well as the integration of district heating into a future smart energy system based on renewable energy sources. Following a review of recent 4GDH research, the article quantifies the costs and benefits of 4GDH in future sustainable energy systems. Costs involve an upgrade of heating systems and of the operation of the distribution grids, while benefits are lower grid losses, a better utilization of low-temperature heat sources and improved efficiency in the production compared to previous district heating systems. It is quantified how benefits exceed costs by a safe margin with the benefits of systems integration being the most important.
By analysing four types of district heating plants, ranging from fully integrated with an electricity system (combined heat and power and electric boiler) to no integration with an electricity system (wood chip boiler), operation and investment incentives for flexible district heating plants under current Danish, Finnish, Norwegian and Swedish framework conditions have been investigated. Hourly-based operation optimisation over 20 years using the modelling software energyPRO showed that the largest investment incentive in Finland, Norway and Sweden was for combined heat and power with an electric boiler. This is largely driven by subsidies. Conversely, the less-subsidised Danish case incentivised investment in wood chip boilers. Untaxed biomass is the major energy source in all scenarios, while electricity use is limited. Capacity component-based tariffs can eliminate operation of electric boilers, while less costly energy component-based tariffs can increase the operation of electric boilers. Heat storage was found to be a no-regrets solution for optimising operation and lowering costs in all cases.
Recent improvements in low-temperature heat-to-power (LTHtP) technologies have led to an increase in efficiency at lower temperatures and lower cost. LTHtP has so far not been used in district heating. The aim of the study is to establish under what conditions the use of existing LTHtP technology is technically and economically feasible using a district heating system as the heat source. The organic Rankine cycle (ORC) is identified as the most interesting LTHtP technology, due to its high relative efficiency and the commercial availability of devices operating at temperatures in the district heating operating range. The levelised cost of electricity of several ORC devices is calculated for temperatures found in district heating, assuming a zero cost of heat. A case study from Sweden is used to calculate the levelised cost of electricity, the net present value and payback period, based on income from the electricity produced, excluding taxes. Hourly spot market electricity prices from 2017 are used, as well as forecast scenarios for 2020, 2030 and 2040. A sensitivity study tests the importance of electricity price, cost of heat and capital/installation cost. Based on the case study, the best levelised cost of electricity achieved was 26.5 EUR/MWh, with a payback period greater than 30 years. Under current Swedish market conditions, the ORC does not appear to be economically feasible for use in district heating, but the net present value and payback period may be significantly more attractive under other countries’ market conditions or with reduced capital costs. For a positive net present value in the Swedish market the capital cost should be reduced to 1.7 EUR/W installed, or the average electricity price should be at least 35.2 EUR/MWh, if the cost of heat is zero. The cost of heat is an important factor in these calculations and should be developed further in future work.
Renewable energy generation depresses electricity spot prices, which is often used as argument to justify incentives provided to renewables. In the so-called “merit-order effect”, renewable power reduces the load available for conventional power and displaces higher marginal cost generation out of the market. In this study, we estimate the value of the “merit-order effect” due to wind power generation in the Iberian market, in the period between 1st January 2008 and 31st October 2016. This value, representing consumers’ potential cost savings, is compared with the direct costs of the financial incentives in Portugal and in Spain.
The accumulated “merit-order effect” amount is estimated to be 26.1 billion €, whilst the total values for the financial incentives reported is 23.9 billion €. The value of the “merit-order effect” explains the existing lower returns by conventional generation and might have additional impacts on future RES projects, subject to normal electricity market risks.
A series of transformations in heat and cold distribution systems is undergoing with the introduction of 4th generation District Heating and Cooling (DHC) technologies. At the center of this process is the integration of renewable technologies, such as solar heating, geothermal systems with large heat pumps and cooling from natural water formations. In this context, smart DHC systems are designed and early prototype implementations are demonstrated in sites across the world. The purpose of this paper is to trace the latest advancements in existing DHC networks and to identify early smart city technologies incorporated. A summary of basic components and characteristics is attempted with focus on thermal storage technologies coupled with renewable heating and cooling.
This paper compares different strategies to transform the heating sector into a future 100% renewable energy solution. It focuses on the consequences for infrastructures in terms of grids and storage across the electricity, gas and heating sectors. The hypothesis is that these consequences are rarely taken into proper consideration, even though the costs are significant and differ substantially between the alternative pathways. While the smart grid scenarios are based on electricity as an energy carrier, the “smart energy systems” approach is based on a cross-sectoral use of all grids. Using Denmark as a case, this paper shows how the current gas and district heating grids each have twice the capacity of the electricity distribution grid. Moreover, the existing gas and thermal storage capacities are substantially higher and the additional future capacities are more affordable than within the electricity sector. The conclusion is that the “smart grid” pathway requires a 2–4 times expansion of the electricity grid and significant investments in electricity storage capacities, while the “smart energy systems” pathway can be implemented with relatively few investments in affordable minor expansions of existing grids and storage capacities.