Fjärrvärme är den vanligaste uppvärmningsformen i Sverige. Mer är 50 % av alla lokaler och bostäder använder fjärrvärme som värmekälla. Fjärrvärmens grundtanke är att centralisera värmeproduktionen och producera värme storskaligt vid ett eller flera värmeverk. Värmen från värmeverken distribueras till lokaler och bostäder med värmebehov via fjärrvärmeledningar som ligger nedgrävda i marken. Hos varje kund som är ansluten till fjärrvärmenätet finns en fjärrvärmecentral med en värmeväxlare. I värmeväxlaren överför fjärrvärmevattnet värme till fastighetens interna värmesystem. Det avkylda fjärrvärmevattnet pumpas tillbaka i en returledning till värmeverket för att värmas upp igen. Fjärrvärmens distributionssystem utvecklats genom åren. I Sverige är systemen förlagda med andra och tredje generationens fjärrvärme. Tredje generationen fjärrvärme som är den metod som används idag. För att sänka utsläpp från produktionsanläggningar och uppnå nationella och globala miljömål jobbar energibolagen för att uppnå effektivare energianvändning. Ett sätt att effektivisera energianvändningen är att minimera värmeförlusterna i fjärrvärmens distributionssystem. Det här examensarbetet har för avsikt att undersöka skillnader i värmeförluster hos fjärrvärmesystem av andra generationen och fjärrvärmesystem av tredje generationen. Arbetet undersöker om det lönar sig ekonomiskt att byta ut de äldre ledningarna mot nyare med avseende på värmeförlusterna. Simuleringar av de olika systemen redovisar energiförlusterna i framledningen i systemet i watt per meter. Återbetalningstid och miljöpåverkan beräknas med hjälp av indata från energibolagen. Energibolagen har bistått med indata genom en strukturerad intervju. Resultatet visar sig att värmeförlusterna inte kan motivera utbyte av ledningarna på egen hand. Tillsammans med andra faktorer så som vattenläckage och reparationskostnader kan värmeförlusterna vara en viktig faktor att ta hänsyn till i en reinvesteringskalkyl. Med tanke på fjärrvärmeproduktionens främst använder förnybara bränslen går det inte heller att se några positiva miljöeffekter genom att byta ut ledningarna. / District heating is the most common type of heating in Sweden. More than 50% of all premises and homes use district heating as a heat source. The concept of District heating is to centralize heat production and produce heat on a large scale at one or more heating plants. The heat from the heating plants is distributed to premises and houses with heat demand via district heating pipes that are buried in the ground. At each customer who is connected to the district heating network, there is a district heating center with a heat exchanger. In the heat exchanger, the district heating water exchange heat to the property's internal heating system. The cooled district heating water is pumped back into a return line to the heating plant to be reheated.   The district heating distribution system has developed over the years. In Sweden, the sytems in use is second-generation district heating and third-generation district heating, which is the method used today. In order to reduce emissions from production facilities and achieve national and global environmental goals, the energy companies work to achieve more efficient energy use. One way of making energy use more efficient is to minimize the heat losses in the district heating distribution system. This thesis aims to investigate differences in heat losses of second-generation district heating systems and third-generation district heating systems. The work investigates whether it is economically feasible to replace the older lines with regard to heat losses.   Simulations of the various systems calculates the energy losses in the supply system in watts per meter. Repayment time and environmental impact are calculated using input data from the energy companies. The energy companies have assisted with input through a structured interview.   The result shows that the heat losses cannot justify the exchange of the pipes on their own. Along with other factors such as water leakage and repair costs, heat losses can be an important factor to consider in a reinvestment calculation. Considering district heat production mainly using renewable fuels, it is not possible to see any positive environmental effects either by replacing the pipes.

Intermittent nature of renewable energy sources like the wind and solar energy poses new challenges to harness and supply uninterrupted power for consumer usage. Though, converting energy from these sources to useful forms of energy like electricity seems to be promising, still, significant innovations are needed in design and construction of wind turbines and PV arrays with BS systems. The main focus of this research project is mathematical modelling and control of wind turbines, solar photovoltaic (PV) arrays and battery storage (BS) systems. After careful literature review on renewable energy systems, new developments and existing modelling and controlling methods have been analysed. Wind turbine (WT) generator speed control, turbine blade pitch angle control (pitching), harnessing maximum power from the wind turbines have been investigated and presented in detail. Mathematical modelling of PV arrays and how to extract maximum power from PV systems have been analysed in detail. Application of model predictive control (MPC) to regulate the output power of the wind turbine and generator speed control with variable wind speeds have been proposed by formulating a linear model from a nonlinear mathematical model of a WT. Battery chemistry and nonlinear behaviour of battery parameters have been analysed to present a new equivalent electrical circuit model. Converting the captured solar energy into useful forms, and storing it for future use when the Sun itself is obscured is implemented by using battery storage systems presenting a new simulation model. Temperature effect on battery cells and dynamic battery pack modelling have been described with an accurate state of charge estimation method. The concise description on power converters is also addressed with special reference to state-space models. Bi-directional AC/DC converter, which could work in either rectifier or inverter modes is described with a cost effective proportional integral derivative (PID/State-feedback) controller.

This report was written as an examination thesis at Mälardalen University in Västerås, Sweden. The subject of the report is to research whether a theoretical single-family home with already installed district heating system for heating and hot water supply should keep it, replace it with a geothermal heat pump or keep it and use it in combination with a smaller air to water heat pump. The report is uses data from the Swedish government institute of Boverket (Literally: Living institute), along with other sources, to construct a model of an average Swedish house and simulate it in the energy simulation program IDA-ICE. This simulation yielded the data needed to conclude which system would be the cheapest in the long run. Thereafter LCC calculations on said data was used to the determine the cost over a 20-year period for the given systems. The report concludes that the model corresponds with the results from Boverket and is therefore an accurate representation of an average single-family home and that the district heating should be replaced with a geothermal heat pump.

Commercial Buildings and complexes are no longer just national heat and power network energy loads, but they are becoming part of a smarter grid by including their own dedicated local heat and power generation. They do this by utilising both heat and power networks/micro-grids. A building integrated approach of Combined Heat and Power (CHP) generation with photovoltaic power generation (PV) abbreviated as CHPV is emerging as a complementary energy supply solution to conventional (i.e. national grid based) gas and electricity grid supplies in the design of sustainable commercial buildings and communities. The merits for the building user/owner of this approach are: to reduce life time energy running costs; reduce carbon emissions to contribute to UK’s 2020/2030 climate change targets; and provide a more flexible and controllable local energy system to act as a dynamic supply and/or load to the central grid infrastructure. The energy efficiency and carbon dioxide (CO2) reductions achievable by CHP systems are well documented. The merits claimed by these solutions are predicated on the ability of these systems being able to satisfy: perfect matching of heat and power supply and demand; ability at all times to maintain high quality power supply; and to be able to operate with these constraints in a highly dynamic and unpredictable heat and power demand situation. Any circumstance resulting in failure to guarantee power quality or matching of supply and demand will result in a degradation of the achievable energy efficiency and CO2 reduction. CHP based local energy systems cannot rely on large scale diversity of demand to create a relatively easy approach to supply and demand matching (i.e. as in the case of large centralised power grid infrastructures). The diversity of demand in a local energy system is both much greater than the centralised system and is also specific to the local system. It is therefore essential that these systems have robust and high performance control systems to ensure supply and demand matching and high power quality can be achieved at all times. Ideally this same control system should be able to make best use of local energy system energy storage to enable it to be used as a flexible, highly responsive energy supply and/or demand for the centralised infrastructure. In this thesis, a comprehensive literature survey has identified that there is no scientific and rigorous method to assess the controllability or the design of control systems for these local energy systems. Thus, the main challenge of the work described in this thesis is that of a controller design method and modelling approach for CHP based local energy systems. Specifically, the main research challenge for the controller design and modelling methodology was to provide an accurate and stable system performance to deliver a reliable tracking of power drawn/supplied to the centralised infrastructure whilst tracking the require thermal comfort in the local energy systems buildings. In the thesis, the CHPV system has been used as a case study. A CHPV based solution provides all the benefits of CHP combined with the near zero carbon building/local network integrated PV power generation. CHPV needs to be designed to provide energy for the local buildings’ heating, dynamic ventilating system and air-conditioning (HVAC) facilities as well as all electrical power demands. The thesis also presents in addition to the controller design and modelling methodology a novel CHPV system design topology for robust, reliable and high-performance control of building temperatures and energy supply from the local energy system. The advanced control system solution aims to achieve desired building temperatures using thermostatic control whilst simultaneously tracking a specified national grid power demand profile. The theory is innovative as it provides a stability criterion as well as guarantees to track a specified dynamic grid connection demand profile. This research also presents: design a dynamic MATLAB simulation model for a 5-building zone commercial building to show the efficacy of the novel control strategy in terms of: delivering accurate thermal comfort and power supply; reducing the amount of CO2 emissions by the entire energy system; reducing running costs verses national rid/conventional approaches. The model was developed by inspecting the functional needs of 3 local energy system case studies which are also described in the thesis. The CHPV system is combined with supplementary gas boiler for additional heating to guarantee simultaneous tracking of all the zones thermal comfort requirements whilst simultaneously tracking a specified national grid power demand using a Photovoltaics array to supply the system with renewable energy to reduce amount of CO2 emission. The local energy system in this research can operate in any of three modes (Exporting, Importing, Island). The emphasise of the thesis modelling method has been verified to be applicable to a wide range of case studies described in the thesis chapter 3. This modelling framework is the platform for creating a generic controlled design methodology that can be applied to all these case studies and beyond, including Local Energy System (LES) in hotter climates that require a cooling network using absorption chillers. In the thesis in chapter 4 this controller design methodology using the modelling framework is applied to just one case study of Copperas Hill. Local energy systems face two types of challenges: technical and nontechnical (such as energy economics and legislation). This thesis concentrates solely on the main technical challenges of a local energy system that has been identified as a gap in knowledge in the literature survey. The gap identified is the need for a controller design methodology to allow high performance and safe integration of the local energy system with the national grid infrastructure and locally installed renewables. This integration requires the system to be able to operate at high performance and safely in all different modes of operation and manage effectively the multi-vector energy supply system (e.g. simultaneous supply of heat and power from a single system).

This thesis develops an analytical model of a small-scale solar central receiver power plant located at the California Polytechnic State University in San Luis Obispo, California at 35.28° N, 120.66° W. The model is used to analyze typical energy output at any time during the year. The power plant is designed to produce an output of 100 kW electrical power, and is supplemented by the combustion of natural gas. Methodologies for determining the proper size and layout of heliostats, optimal tower height, receiver size, and turbine engine selection are developed. In this specific design, solar shares of up to 73.2% and an annual average of 44% are possible through the use of a gas-solar hybrid microturbine engine. Larger solar shares are not possible due to the limited size of land (about 0.5 acres used for this project) which limits the number of possible heliostat installations.

Energy company Vattenfall AB has set an ambitious goal in trying to transform their business into a climate neutral and more resource effective company - all within thetime frame of one generation. Through the business concept “SamEnergi” within the heat sector, Vattenfall looks for district heating customers who are willing to sell heat at a price corresponding to Vattenfall’s own production cost. This report examines the change in ordinary heat production in the partially Vattenfall-owned district heating system in the city of Ludvika, Sweden, arising from the delivery of waste heat from a data center to the system. The data center delivers 1 MW heat at 60 degrees Celsius which is lower than the desired temperature in the district heating grid. The change in ordinary heat production is evaluated in four different scenarios where each scenario represents a way to connect the heat source to the district heating grid, so that the delivery temperature to the costumer is not affected. In two of the scenarios, the data center is placed on site of the main heat production units. For all four scenarios, the ability to deliver heat during normal annual fluctuations in flow and temperature in the district heating grid are assessed. Also, in one scenario the effecton a flue gas condenser is considered. The heat production for a normal year is then modelled and simulated using an optimization software called BoFiT, with and without the excess heat. The results show that the 1 MW excess heat is worth between 0,9 and 1,8 million SEK depending on how the heat is delivered. The lowest value of the excess heat source comes from the scenario requiring a heat pump. The other three scenarios yield similar savings on the ordinary production. The best scenario is when the waste heat is delivered together with the main production unit.

The built environment is responsible for a large portion of total energy use and emissions. A large portion comes from the buildings themselves, but also the transportation system to move people around. As global populations grow, and more people migrate to cities, it is critically important that new city growth is done in the most sustainable manner possible. The typical North American pattern of urban growth is urban sprawl, characterized by single use type zoning, low density, transportation system dominated by personal vehicles, and poor public transit. Urban sprawl has numerous downsides, including poorer energy efficiency in buildings and infrastructure, more congestion and higher emission from vehicles, as well as many negative health effects. This thesis presents the concept of a Mothership, a large, high-density mixed-use building designed to combat urban sprawl and minimize energy use and emissions of the built environment. A mothership is designed to provide all the amenities and housing of a typical suburb for 10,000 people. The analysis in this thesis employ building simulation tools to model various mothership designs and analyse the operational and embodied energy and carbon emissions for each design, and compare it to base cases of more traditional building use types such as single detached homes, and different types of apartment buildings. The effect of high-performance building envelopes and other building materials on operational and embodied energy and emissions are analysed. A multi objective optimization analysis is performed to determine which technologies and combinations of technologies provide the lowest cost solution to meet the mothership’s energy demands while also minimizing emissions. The mothership’s effect on transportation emissions is also investigated. The building’s mixed-use nature allows trips to be satisfied within walking distance in the building. The high concentration of people makes for a good anchor load for public transportation, so the emissions reductions of implementing a bus rapid transit system from the mothership to the central business district is estimated. To reduce transportation emissions further, the effect of an electric car share fleet for mothership residents use is also quantified. The energy system of a mothership is optimized, along with base cases of single detached homes, under numerous scenarios. These scenarios are designed to explore how the energy system changes in an attempt to answer a series of research questions. Some of the measures explored are a high carbon tax, net metering, and emissions limits of net zero, and negative emissions with two different electrical grid carbon intensities. Results showed that a highly insulated, timber framed mothership can achieve very high reductions in energy use and emissions. Overall it showed reductions of 71%, 73%, and 74% in operational energy, embodied energy and embodied carbon respectively, over a baseline case of single detached homes. It was estimated that transportation emissions could be reduced by 58% through the mixed-use development reducing the number of trips and electrically powered transportation vehicles and bus rapid transit. This gives a combined total emissions reduction of 61%. Energy system optimization showed that the mothership design in achieved far lower costs and emissions (4 and 8.7 times lower respectively) than the base case of single detached homes. Of the mothership cases examined, the most expensive case was the one which had a carbon tax, with an annualized cost of $4.3 million. The case with the lowest annualized cost was one with, among other factors, a net zero carbon emissions restriction (annualized cost of $3.08 million. Many of the cases had negative operating costs due to the sale of renewable energy or carbon credits. This illustrates that the integration of renewable energy technologies is not only beneficial for reducing emissions but can also act as an income pathway for energy systems. / Graduate

This thesis reports findings from a number of modern machine learning techniques applied to electricity market price forecasting. The techniques evaluated were Support Vector Machines, Boosting, Bayesian networks, neural networks and a weekly average method. All techniques were evaluated on seven day into the future forecasting of the Regional Reference (pool) Prices (RRP) for the New South Wales (NSW) region of the Australian National Electricity Market (NEM). Due to highly volatile and non-repetitive nature of the NSW RRP, all complex machine learning methods provided inferior accuracy forecasts compared to a weekly average method. The weekly average method was computationally less expensive and more transparent to the user than any of the machine learning techniques. The Support Vector Machine (SVM) was chosen for its novel application to electricity price forecasting because it is considered to be the next generation to neural networks. The structured SVM training algorithm proved more consistent and reliable than the neural network algorithm. Bayesian networks offer the adaptability of a neural network with the advantage of providing a price forecast with confidence intervals for each half-hour determined from the actual data. The SVM and Bayesian techniques were found to provide acceptable forecasts for NSW demand. An investigation of international electricity markets found that each market was unique with different market structures, regulations, network topologies and ownership regimes. Price forecasting techniques and results cannot be universally applied without careful consideration of local conditions. For instance, price data for the Spanish and Californian electricity markets were investigated and found to have significantly lower price volatility than the NSW region of the NEM. An extensive examination of the NSW RRP showed that the price exhibited no consistent long-term trend. A stationary data set could not be extracted from the price data. Thus, making forecasting unsuited to techniques using large historical data sets. The strongest pattern found for NSW prices was the weekly cycle, so a weekly average method was developed to utilise this weekly cycle. Over 25 weeks of NSW RRP from February to July 2002, the seven day into the future price forecast mean absolute error (MAE) for the SVM technique was 27.8%. The weekly average method was more accurate with an MAE of 20.6% and with a simple linear price adjustment for demand, the error was reduced to 18.1%. The price spikes and uneven distribution of prices were unsuitable for the Boosting or Bayesian network techniques.

The research conducted in this thesis provides a robust implementation of a preconditioned iterative linear solver on programmable graphic processing units (GPUs). Solving a large, sparse linear system is the most computationally demanding part of many widely used power system analysis. This thesis presents a detailed study of iterative linear solvers with a focus on Krylov-based methods. Since the ill-conditioned nature of power system matrices typically requires substantial preconditioning to ensure robustness of Krylov-based methods, a polynomial preconditioning technique is also studied in this thesis. Implementation of the Chebyshev polynomial preconditioner and biconjugate gradient solver on a programmable GPU are presented and discussed in detail. Evaluation of the performance of the GPU-based preconditioner and linear solver on a variety of sparse matrices shows significant computational savings relative to a CPU-based implementation of the same preconditioner and commonly used direct methods.

The research conducted in this thesis provides a robust implementation of a preconditioned iterative linear solver on programmable graphic processing units (GPUs). Solving a large, sparse linear system is the most computationally demanding part of many widely used power system analysis. This thesis presents a detailed study of iterative linear solvers with a focus on Krylov-based methods. Since the ill-conditioned nature of power system matrices typically requires substantial preconditioning to ensure robustness of Krylov-based methods, a polynomial preconditioning technique is also studied in this thesis. Implementation of the Chebyshev polynomial preconditioner and biconjugate gradient solver on a programmable GPU are presented and discussed in detail. Evaluation of the performance of the GPU-based preconditioner and linear solver on a variety of sparse matrices shows significant computational savings relative to a CPU-based implementation of the same preconditioner and commonly used direct methods.