The increasing demand for renewable energy sources to meet climate targets and reduce carbon emissions poses challenges to the power grid due to their intermittent nature. One potential solution to maintain grid stability is by implementing Hybrid Energy Systems (HESs) that incorporate a Battery Energy Storage System (BESS). To achieve the most favorable outcome in terms of both technical feasibility and profitability of a BESS, it is essential to employ models for simulating and optimizing the control of system components. This thesis focuses on the analysis of energy and revenue streams in a HES consisting of a BESS, photovoltaics (PVs), and an energy load including a fast charging station for electric vehicles (EVs). The objective is to optimize the system based on revenue generation by comparing the control techniques of peak shaving, energy arbitrage, and the integration of ancillary services within the Swedish energy market. The research questions explore the optimal utilization of the BESS and assess the impact of the different control techniques. A model is created in Python with the package CasADi where data from an ongoing installation of a HES in southern Sweden is combined with data from literature research. The model includes an objective function that minimizes the total cost of power from the grid based on the day-ahead price, battery degradation, and monthly peak power.  To answer the research questions, four different scenarios are simulated. The first scenario is a base for comparison, the second one focuses on peak shaving and energy arbitrage, the third on participation in the ancillary service FCR-D upwards regulation, and the last one is a combination of peak shaving, energy arbitrage, and the ancillary service FCR-D. The results show that the remuneration from the ancillary service FCR-D is comparably much higher than the revenues generated from peak shaving and energy arbitrage, providing more than 500% of revenue compared to the same system but without a BESS. The scenario with peak shaving and energy arbitrage shows an increase in revenue of 29% but with more cycling of the battery which could cause losses in performance in the long term. To validate the results, sensitivity analyses are conducted by evaluating weighting in the objective function, implementing Model Predictive Control (MPC), and reviewing price variations.  In conclusion, efficient control techniques can enhance system performance, minimize losses, and ensure optimal utilization of different energy sources, leading to improved feasibility and profitability. The optimal usage of a BESS involves finding a balance between maximizing revenue generation and minimizing battery degradation. This can be achieved through control strategies that optimize the charging and discharging patterns of the BESS based on electricity price signals, demand patterns, and battery health considerations.

The goal of achieving carbon neutrality for year 2045 puts Sweden on a road towards further implementation of renewable energy into their energy system. The goal of this degree project is to investigate how the implementation of renewable energy in Sweden is worked towards on a regional level but also to explore how this expansion might look for the Norra Småland Region. By conducting several semi-structured interviews with the energy agencies of Sweden as well as creating a model for the potential solar and wind power expansion in the Norra Småland Region, the authors aim to answer the questions of how the current work with expanding the renewable energy share in the Swedish energy system is conducted; what bottlenecks, problems and challenges exist and what tools, data, information and incentives might help further facilitate this work. Through the interviews conducted, this degree project encapsulates the different projects related to the subject of renewable energy which the various regions of Sweden work with. The degree project also provides a compilation of various challenges related to the projects as well as an insight into what tools and incentives are asked for by the regions with the goal of helping facilitate the work conducted. The degree project also provides a projection for how the future expansion of wind power and solar power might look in the Norra Småland Region based on the previous expansion trends, resulting in three individual projections with individual growth rates.

<p>Solar thermosiphons integrated into the thermal envelop of buildings has been studied for their potential to take advantage of solar energy in heating buildings. The annual performance of the Solar Thermal Buffer Zone cannot currently be predicted with the correlations from previous research. Also, no work has been done on using the thermal buffer zone with a natural convection for energy savings in a building even though it has the potential to provide heating. The goal of this project was to design, analyze and determine the feasibility of a thermal buffer zone in a building. A thermal buffer zone can be defined as a fluid filled cavity which envelopes a building. This cavity provides a building with additional insulation but also allows for collection of solar energy and to be distributed throughout the structure in order to heat the interior. To show the physical aspect, the flow visualization in the project, computational fluid dynamic (CFD) software was used which was experimentally not possible. A physical prototype was then designed and constructed in order to test the effectiveness of the TBZ.</p> <p>This experiment included radiation as the heat source and the ability to vary geometric lengths. The performance parameters of mass flow rate were comparable between the numerical predictions and experimental results. However, due to uncertainties in the current experimental setup, full validation of the numerical model was not possible. These uncertainties would have to be addressed before the numerical model that was developed can be fully validated and used for generating correlations.</p> / Master of Applied Science (MASc)

<p>Among the six GEN-IV reactor concepts recommended by the Gen-IV International Forum, supercritical water-cooled reactors (SCWR) have gained significant interests due to its economic advantage, technology and experience continuity. In the last few years, extensive R&D activities have been launched covering the various aspects of SCWR development, especially in thermal-hydraulic analysis. In Canada, most R&D projects are led by AECL or NRCan.</p> <p>SCWR design and development require the modification of simulation codes used for design and safety demonstration of subcritical water-cooled reactors. This study modifies the subchannel code COBRA-TF, applicable to only subcritical water-cooled reactors, to a new version COBRA-TF-SC, applicable to both supercritical and subcritical water-cooled reactors. Supercritical water property data tables and supercritical water property formulations are implemented. Supercritical water heat transfer and pressure drop correlations are also added. The saturation curve in the subcritical model is extended by introducing a pseudo two-phase region at supercritical pressures to avoid any numerical instabilities consistent with other studies.</p> <p>Some simple fuel bundle experimental data on the flow and temperature distribution are used to evaluate the code. The fuel bundle experiment is simulated with both COBRA-TF-SC and AECL's ASSERT-PV-SC. The COBRA-TF-SC predicted results show good agreement with the experimental data and results obtained from ASSERT-PV-SC, demonstrating good feasibility and accuracy of this code. COBRA-TF-SC is then used to predict the detailed thermalhydraulics behaviour of the 62-element Canadian SCWR fuel bundle design. The advantage of COBRA-TF-SC is that it can accommodate transcritical flow conditions whereas the existing subchannel codes for SCWRs cannot.</p> / Master of Applied Science (MASc)

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components. <div><br></div><div>This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixed-integer linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. </div><div><br></div><div>The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization. Around 3.5% cost savings is achievable with both MILP optimization cases while almost 10.7% cost savings is achieved using the hybrid MILP-NLP approach compared to the current plant operation. For the selected components from MILP optimization, NLP balances the equipment performance to operate at the state point where its efficiency is maximum while still meeting the demand. Using this hybrid approach, a high-quality global solution is determined when the linear model is feasible while still taking into account the nonlinear nature of the problem. </div><div><br></div><div>Simulations were extended for different seasons to examine the sensitivity of the optimization results to differences in electric, heating and cooling demand. All the optimization results suggest there are opportunities for potential cost savings across all seasons compared to the current operation of the power plant. For a large CCHP plant, this could mean significant savings for a year. The impact of choosing different time range is studied for MILP optimization because any changes in MILP outputs impact the solutions of NLP optimization. Sensitivity analysis of the optimized results to the cost of purchased electricity and natural gas were performed to illustrate the operational switch between steam and electric driven components, generation and purchasing of electricity, and usage of coal and natural gas boilers that occurs for optimal operation. Finally, a modular, generalizable, easy-to-configure optimization framework for the cost-optimal control of large-scale combined cooling, heating and power systems is developed and evaluated.</div>

The thesis outlined in this report is a pre-feasibility study of the potential to use waste-to-energy technology in the region Kutai Kartanegara, Borneo, Indonesia. The project is a collaboration between the Kutai Kartanegara government, Uppsala University, the Swedish University of Agricultural sciences and the technology consulttancy Sweco. The current waste management system in Kutai Kartanegara consists of landfills in the cities and open burnings and dumping in the lesser developed sub-districts. This is a growing problem both environmentally and logistically. The electrification in the sub-districts is sometimes as low as 17 % and access to electricity is often limited to a couple of hours per day. The current electricity production in the region is mainly from fossil fuels. Data was collected during a two month long field study in Tenggarong, the capital of Kutai Kartanegara. From the collected data, various waste-to-energy systems and collection areas were simulated in Matlab. Results from the simulations show that a system using both a waste incineration and biogas plant would be the best solution for the region. The chosen system is designed to handle a total of 250,000 tons of waste annually, collected from Tenggarong and neighbouring districts. The system will provide between 155 and 200 GWh electricity and between 207 and 314 GWh of excess heat energy annually. Some of this is used in a district heating system with an absorption-cooling machine. The system investment cost is around 42.5 MUSD and it is expected to generate an annual profit of 16 MUSD. The recommended solution will decrease the emissions of CO2-equivalents compared to the current waste system and fossil electricity production with 50%. The results in the study clearly show that there are both economic and environmental potential for waste-to-energy technologies in the region. But the waste management and infrastructure has to be improved to be able to utilize these technologies. By implementing waste-to-energy technologies, the supplied waste can be seen as a resource instead of a problem. This would give incentives for further actions and investments regarding waste management.

Skolfastigheter is a municipality owned real estate company that manages most of the buildings used for lower education in Uppsala. The company is working in line with the environmental goals of the municipality by installing photovoltaic systems in schools and other educational buildings. Skolfastigheter are planning to install a photovoltaic system in a school in Stenhagen. The purpose of this study is to optimally design the proposed system. The system will be maximized, which in this study entails that the modules will be placed on every part of the roof where the insolation is sufficient. The system will also be grid connected. The design process includes finding an optimal placement of the modules, matching them with a suitable inverter bank and evaluating the potential of a battery storage. Economic aspects such as taxes, subsidies and electricity prices are taken into account when the system is simulated and analyzed. A sensitivity analysis is carried out to evaluate how the capacity of a battery bank affects the self-consumption, self-sufficiency and cost of the system. It is concluded that the optimal system has a total peak power of almost 600 kW and a net present value of 826 TSEK, meaning that it would be a profitable investment. A battery bank is excluded from the optimal design, since increasing the capacity of the bank steadily decreased the net present value and only marginally increased the self-consumption and self-sufficiency of the system.

In response to growing concerns regarding global warming and climate change, reduction of CO2 emissions becomes a priority for many countries, especially the developed ones such as the UK. Residential applications are considered among the most important areas for substantial reduction of CO2 emissions because they represent a major part of the total consumed energy in those countries. For instance, in the UK, residential applications are currently accountable for about 150 Mt CO2 emissions, which represents approximately 25% of the whole CO2 emissions [1-2]. In order to achieve a significant CO2 reduction, many strategies must be adopted in the policy of these countries. One of these strategies is to introduce micro combined heat and power (μCHP) systems into residential energy systems, since they offer several advantages over traditional systems. A significant amount of research has been carried out in this field; however, in terms of integrating such systems into residential energy systems, significant work is yet to be conducted. This is because of the complexity of these systems and their interdependency on many uncertain variables, energy demand of a house is a case in point. In order to achieve such integration, this research focuses on the optimisation and operation of μCHP systems in residential energy systems as essential steps towards integration of these systems, so it deals with the optimisation and operation of a μCHP system within a building taking into account that the system is grid-connected in order to export or import electricity in certain cases. A comprehensive review that summarises key points that outline the trend of previous research in this field has been carried out. The reviewed areas include: technologies used as residential μCHP units, modelling of the μCHP systems, sizing of μCHP systems and operation strategies used for such systems. To further this, a generic model for sizing of μCHP system’s components to meet different residential application has been developed by the author. Two different online operation strategies of residential μCHP systems, namely: an online linear programming optimiser (LPO) and a real time fuzzy logic operation strategy (FLOS) have been developed. The performance of the novel online operation strategies, in terms of their ability to reduce operation costs, has been evaluated. Both the LPO and the FLOS were found to have their advantages when compared with the traditional operation strategies of μCHP systems in terms of operation costs and CO2 emissions. This research should therefore be useful in informing design and operation decisions during developing and implementing μCHP technologies in residential applications, especially single dwellings.

Energy is an important input for various activities that provide impetus to economic, human and social development of any country. Among all the energy carriers, electricity is the most important and sought after energy carrier for its quality, versatility and ability to perform various technology driven end-use activities. Therefore access to electricity is considered as the single most important indicator determining the energy poverty levels prevailing in a country. Demand for electricity has increased significantly, especially in the developing countries, in recent years due to growth in population and intensification of economic activities. Therefore, providing quality and reliable electricity supply at low-cost has become one of the most pressing challenges facing the developing world. Although sufficient efforts have gone into addressing this issue, little progress has been made in finding a satisfactory solution in alleviating this problem. Currently, electricity supply is mostly dependent on centralized large-scale power generation. These centralized systems are strongly supply focused, fossil-fuel intensive, capital intensive, and rely on large-distance transmission and distribution systems. This results in electricity cost becoming unaffordable to the majority poor which comprises more than 70% of the total population in developing countries like India and the benefits of quality energy remaining with the rich, giving rise to inequitable distribution of energy. Continuous exploitation of fossil fuels has also contributed to local and global pollution. Therefore it is necessary to explore alternate means of providing energy access such that the energy carriers are clean, easy to use, environmentally benign and affordable to the majority of the rural poor. India is at a critical juncture of passing through the path of development. India is also in a unique position that its vast majority of rural population is energy poor which is disconnected from the electricity grid. In this context, the proposed research is an attempt towards developing a greater understanding on the issue of rural energy access and providing a possible solution for addressing this gap. This has been proposed to be achieved by adopting a decentralized energy planning approach and distributed energy systems mostly based on renewable energy sources. This is expected to reduce dependence on imported energy, promote self-reliance, provide economically viable energy services for rural applications and be environmentally safe. The focus is limited to biomass energy route which has many advantages; it is a geographically equitably distributed resource, geographical advantage of having potential to setup energy systems at any location where vegetation is present and not seasonal like other renewable energy technologies. A mathematical model-based approach is developed to assess the feasibility of such a proposal. Models are developed for performing biomass resource assessment, estimating end-use-wise hourly demand for electricity, performing capacity and location planning and assessing economic feasibility. This methodological framework was validated through a case study developed for the district of Tumkur in the state of Karnataka (a state in southern region of India). The literature survey was conducted exhaustively covering the whole span of supplyside and demand-side management of electricity systems, and grid-connected and stand-alone power generation systems, their technical, economic and environmental feasibilities. Literature pertinent to GIS applications in biomass assessment, facility location planning and scheduling models were also reviewed to discern how optimal capacity, location and economic dispatch strategy was formulated. Through the literature survey it was understood that there were very few attempts to integrate both demand-side management and supply-side management aspects in the rural energy context. GIS based mathematical models were sparsely used in rural energy planning and decision making. The current research is an attempt to bridge these gaps. The focus in this study is on effectively utilizing the locally available biomass resource. Assessment of Biomass Potential for Power Generation As a first step, the supply option was studied at village level by overlaying LULC (land use land cover) and village boundary GIS maps of Tumkur district. The result was fortified by the NDVI results from remote sensing images of land use pattern in Tumkur district. A detailed village-level assessment of wasteland potential was made for the entire district. The result showed which shows that in Tumkur district, roughly 17.3% of total geographical land was under exploitable wasteland. Using secondary data and literature, biomass potential indices were prepared for different wasteland types to determine the total biomass potential for power generation. The results based on the GIS data the assessment shows that Tumkur has roughly 17.3% of exploitable wasteland. A complete village-level annual power generation potential was assessed considering both energy plantations from wasteland, existing degraded forests and crop residues. Assessment of end-use-wise hourly Demand for Electricity at Village Level Household survey was conducted for 170 sample households randomly chosen from 15 villages, again randomly selected to represent different socio-economic categories. Using statistical tools like k-means clustering, one-way ANOVA and Tukey’s HSD test, first the households were classified into three economic categories to study the distribution of the households in each sample village. Later based on the number of households of each type in a village, the villages were further classified into five groups based on their socio-economic status. This was done to select the right representative per-household power demand for a village of any particular socioeconomic category. The representative per household power demand in each economic category along with secondary data helped in deriving the electricity daily load profiles for all the villages. Representative demand profiles were generated for different seasons across different sectors namely domestic, agriculture and industry sectors at the end-use level comprising of activities like home lighting, appliances, irrigation pump sets operation and small industry operations. Mathematical Modeling for Optimal Siting of Biomass Energy Systems Since the power has to be generated through biomass route, biomass may have to be transported over a large geographical area which requires efficient design of logistic systems. Apart from that, a major component of cost of biomass power is the cost of transportation of biomass from source to the power plant. Therefore it is important to determine the optimal siting of biomass energy systems to minimize the cost of transportation. Since these optimal locations are based on minimizing Euclidian distance, installing the power generation systems at these locations would also minimize total cost of local transmission and distribution. In order to locate the biomass energy system, K-medoid clustering algorithm was used to determine the optimal number of clusters of villages to minimize the Euclidean distance between the medoid of the cluster and the villages within the cluster, and minimize the total installed capacity to meet the cluster demand. The clustering algorithm was modified in such a way that the total capital cost of the power generation system installation was minimized. Since the total project cost not only depended on capital cost alone, but also on biomass transportation and power transmission costs, these costs were also included in the analysis. It was proposed to locate the energy systems at the medoids of the clusters. Optimal Capacity Planning Installing biomass power systems requires large investments. It is therefore necessary to reduce the peak demand to bring down the installed capacity required. This was achieved by developing heuristics to arrive at an optimal scheduling scheme of the end-use activities that would minimize the peak demand. The heuristics procedure was demonstrated on five representative villages, each from different economic category. The optimal demand profile was used as input in HOMER micro-energy system simulation software to perform a techno-economic analysis. The simulation facilitated a thorough economic feasibility study of the system. This included a complete analysis of the cash inflows and outflows, capital cost of the system, operation and maintenance cost, cost of fuel and estimation of total GHG emissions. There are many limitations in planning at village-scale. The results indicated that capacity planning done at the village level was prone to over-estimation of installed capacity of the system increasing the investment requirement, under utilization of the capacity and suffered from supply scarcity of biomass. This emphasized the need for looking at a bigger conglomerate of villages in other words cluster of villages. In the next step, the optimal capacity planning was performed for one of the clusters formed using the K-medoid clustering algorithm with the power generation system located at the medoid. For demonstrating the practical feasibility of extending the methodology to cluster level, a cluster with maximum number of villages was chosen from the optimal cluster set in the k-medoid algorithm output. The planning was conducted according to the socioconomic category of the villages in the cluster. Economic implications of Stand-alone (SA) vs Grid-connected (GC) Mode of Operation Other important question that was answered in this analysis was a comparison of GC systems with SA systems. Since extension of grid to a village that is not electrified involved drawing high voltage transmission lines from the nearest grid point, installation of distribution transformers and low transmission lines within the village for distribution. Since these involve high costs it was necessary to study whether or not it is feasible to extend the grid or install a stand-alone system. This question was answered by the breakeven distance for which grid extension becomes more economical than a SA system. For each village breakeven distance varied with the total installed capacity and the operational costs. This helped to compare the GC systems vis-à-vis SA systems from the point of view of economic feasibility. Summary It is necessary that planning and strategies be rational and reasonable for effectively assuaging the rural electrification imbroglio. The current study has highlighted the importance of integrating both demand-side-management and supply-sidemanagement of energy systems in the context of planning for power generation and distribution in rural areas. The key findings in the current study are: • The study showed the feasibility of biomass power systems in meeting the rural electricity needs. • Biomass assessment results showed that, if the power demand could be brought down by replacing the existing appliances with efficient ones (ex. compact fluorescent lamps and improved irrigation pump set valves), Tumkur district has enough biomass potential to meet both the current as well as increased future demands for electricity. • The optimal number of clusters minimizing total capital cost of biomass energy systems, transportation cost of biomass and distribution cost of power, was 96 for Tumkur district. For Kunigal block, the optimal number of clusters was 37 and 32 for supply and demand scenarios 1(BAU -Business As Usual) and 2 (with 10% increase in cropland and 20% increase in demand). • The optimal capacity planning emphasized the importance of clustering of villages for minimizing the total installed capacity. The result also showed that the breakeven distance was the determining factor about the choice of GC vs SA systems. The main contributions of this thesis are: i. Hourly demand pattern was studied to estimate the aggregate demand for electricity at village level for different sectors across various seasons. ii. Village-wise biomass resources potential for power generation was assessed iii. Optimal locations for siting biomass energy systems were identified using k-medoid clustering algorithm iv. An optimal scheduling of end-use activities was planned using heuristics method to minimize the installed capacity v. Optimal location, scheduling plan of end-use activities and optimal capacity were determined for individual villages as well as village clusters vi. The economic implications of grid extension vis-à-vis stand-alone mode of operation of the installed biomass energy systems were studied The generalized, multipronged approach presented in this thesis to effectively integrate both demand-side management and supply-side management in rural energy planning can be implemented for any rural region irrespective of the location. The results emphasized that for efficient demand-side and supply-side management, it is important to plan for clusters of villages than at the individual village level. The results reported in this thesis will help the policy and strategy makers, and governments to achieve rural electrification to a satisfactory extent to ensure continuous, uninterrupted and reliable power supply by determining the clustering strategy, optimal cluster size, optimal scale and siting of decentralized biomass power generation systems.

World energy systems are going through a continuous change. The focus has been shifted from large thermal or hydal power generation to small distributed generation, mainly based upon renewable energy systems. This transition is also backed by some governments. There have also been significant improvements in grid technology, and modern-day smart grid can provide real time bi-directional flow of data i.e. “real time energy deficit and surplus, and also real time prices to both producers and consumers. Smart grid can also accommodate intermittent small suppliers of electricity. This shift in energy generation policy and improvement in grid technology has opened ways for small scale energy producers and consumers to share energy with each other. It has also opened ways to purchase or sale energy to unknown peers over a smart grid. Need has been felt to store these transactions among peers in a secure, non-alterable yet quickly accessible way. Blockchain technology offers to provide this secure, unalterable yet quickly accessible ledger. In this study this transition process and role of blockchain technology for future energy systems has been historically reviewed. It has been found out that on top of keeping record of Peer to Peer transactions, blockchain technology can fill many other purposes. However, technology is still not matured for large scale projects, Research projects are underway to decrease the large time and energy consumption for block building computational processes yet keeping them safe and reliable.