Solar Power and Uninterruptible Power Supply (UPS) are two technologies that are growing rapidly. The demand for solar energy is mainly driven by the trend towards cheaper solar cells, making it economically profitable for a larger range of applications. However, solar power has yet to reach grid parity in many geographical areas, which makes ways to reduce the cost of solar power systems important. This thesis investigates the possibility and potential economic synergies of combining solar power with UPS systems, which have been previously researched only from a purely technical point of view. This thesis instead evaluates the hypothesis that a combined solar and UPS system might save additional costs compared to regular grid-tied systems, even in a stable power grid. The primary reason is that on-line UPS systems rectifies and inverts all electricity, which means that solar energy can be delivered to the DC part of the UPS system instead of an AC grid, avoiding the installation of additional inverters in the solar power system. The study is divided into three parts. The first part is a computer simulation using MATLAB, which has an explorative method and aims to simulate a combined system before experimenting physically with it. The second part consists of experiments on a physical prototype system based on basic UPS and solar power components. The third part is an economical assessment of investment costs and energy balances, comparing two separate systems (UPS and solar power separate) to one combined (UPS & solar power). The results from the prototype system show that adding solar power to an UPS system does not interfere with the UPS functionality in any major way, however for optimal performance some additional integration may be necessary. On the contrary, the additional power terminal that the solar panels constitute, can increase system performance during certain operational conditions. The result of the economic analysis shows that a combined system has potential for both a lower investment cost due to cheaper components and increased energy savings through lower conversion losses. The conclusion from the study is that a combined solar energy and UPS system is technically feasible. Furthermore, a combined system has clear economic advantages over two separate systems. This means that a combined system might be economically profitable even in situations where a separate system is not. / Solenergi och avbrottsfri kraftförsörjning (UPS) är två tekniker som växer snabbt. Efterfrågan på solenergi ökar huvudsakligen på grund av den snabba utvecklingen mot billigare solceller, vilket lett till att solenergi blivit lönsamt i en större mängd applikationer. I många områden är solenergi dock fortfarande inte kostnadsmässigt konkurrenskraftigt jämfört med traditionella energikällor, vilket gör en fortsatt sänkning av kostnaderna för solenergi till en viktig fråga för solenergiindustrin. Detta examensarbete har som syfte att undersöka om det är tekniskt möjligt att kombinera solenergi med UPS-system samt potentialen för ekonomiska synergier med denna kombination. Tidigare forskning inom området har endast undersökt denna kombination från en rent teknisk synvinkel. Detta examensarbete driver istället hypotesen att ett kombinerat solenergi- och UPS-system kan leda till större kostnadsbesparingar jämfört med ett traditionellt nätanslutet solenergisystem, även i ett stabilt elnät som i Sverige. En on-line UPS skyddar en känslig last genom att kontinuerligt likrikta och sedan åter växelrikta inkommande ström för att därmed både isolera lasten från nätet samt höja strömkvalitén. I UPS-systemet finns därmed en likströmsdel dit solpanelerna direkt kan kopplas istället för att skicka den genererade solenergin ut på elnätet. Därmed undviks inköp och installation av sol-växelriktare i solenergisystemet. Studien är uppdelad i tre delar. Första delen är en datorsimulering i MATLAB och syftar till att explorativt undersöka det kombinerade systemet för en optimerad design innan fysiska experiment utförs. Den andra delen av studien utgörs av experiment på ett fysiskt prototypsystem baserat på ett principiellt UPS- och solenergisystem. Den tredje delen av studien är en ekonomisk analys av både investeringskostnader och energibalanser som jämför ett kombinerat system (UPS & sol) med två separata system (UPS & sol separat). Resultaten från prototypsystemet visar att påkopplandet av solceller i en principiell UPS har mycket låg påverkan på UPS-systemets funktionalitet, samt att solcellerna som en extra energikälla under vissa driftförhållanden kan ha en positiv påverkan på UPS-systemet. För optimal prestanda kan dock en viss integration av systemen krävas.  Resultatet från den ekonomiska analysen visar att ett kombinerat system har potential att sänka investeringskostnaden genom billigare komponenter. Ett kombinerat system kan även leda till en högre energibesparing jämfört med ett nätanslutet solenergisystem eftersom konverteringsförlusterna i UPS-systemet sjunker i det kombinerade systemet. Slutsatsen av studierna är att ett kombinerat solenergi- och UPS-system är tekniskt möjligt. Dessutom finns betydande ekonomiska synergier med ett kombinerat system. Detta innebär att ett kombinerat system kan vara lönsamt även i fall där ett separat solelsystem inte är det.

The recent advances in machine learning (ML) have brought revolutionary changes to every field. Many novel applications, such as face recognition and natural language processing, have demonstrated the great potential of ML techniques. Indeed, ML can significantly enhance the intelligence of many existing systems, including smart grid, wireless communications, mechanical engineering, and so on. For instance, microgrid (MG), a distribution-level power system, can exchange energy with the main grid or work in islanded mode, which enables higher flexibility for the smart grid. However, it suffers considerable management complexity by including multiple entities such as renewable energy resources, energy storage system (ESS), loads, etc. In addition, each entity may have unique observations and policies to make autonomous decisions. Similarly, 5G networks are designed to provide lower latency, higher throughput and reliability for a large number of user devices, but the evolving network architecture also leads to great complexity for network management. The 5G network management should jointly consider various user types and network resources in a dynamic wireless environment. In addition, the integration of new techniques, such as reconfigurable intelligent surfaces (RISs), requires more efficient algorithms for network optimization. Consequently, intelligent management schemes are crucial to schedule network resources. In this work, we aim to develop state-of-the-art ML techniques to improve the performance of large-scale systems. As case studies, we focus on MG energy management and 5G radio access network (RAN) management. Multi-agent reinforcement learning (MARL) is presumed to be an ideal solution for MG energy management by considering each entity as an independent agent. We further investigate how communication failures will affect MG energy trading by using Bayesian deep reinforcement learning (BA-DRL). On the 5G side, we use MARL, transfer reinforcement learning (TRL), and hierarchical reinforcement learning (HRL) to improve network performance. In particular, we study the performance of those algorithms under various scenarios, including radio resource allocation for network slicing, joint radio and computation resource for mobile edge computing (MEC), joint radio and cache resource allocation for edge caching. Additionally, we further investigate how HRL can improve the energy efficiency (EE) of RIS-aided heterogeneous networks. The findings of this research highlight the capabilities of various ML techniques under different application domains. Firstly, different MG entities can be well coordinated by applying MARL, enabling intelligent decision-making for each agent. Secondly, Bayesian theory can be used to solve partially observable Markov decision process (POMDP) problems caused by communication failures in MARL. Thirdly, MARL is capable of balancing the heterogeneous requirements of different slices in 5G networks, guaranteeing satisfactory overall network performance. Then, we find that TRL can significantly improve the convergence performance of conventional reinforcement learning or deep reinforcement learning by transferring the knowledge from experts to learners, which is demonstrated over a 5G network slicing case study. Finally, we find that long-term and short-term decisions are well coordinated by HRL, and the proposed cooperative hierarchical architecture achieves higher throughput and EE than conventional algorithms.

Application of Photovoltaic PV panels for electricity production has rapidly increased in recent years in Sweden after launching a capital subsidy for PV panel installations in 2009. Kungsbacka municipality’s housing company equipped two groups of buildings in Fjärås with PV systems to generate electricity. The newly built residential buildings are connected to a DC-microgrid, whereas the existing buildings have been equipped with a single PV system. This project conducts a cradle to gate life cycle assessment (LCA) for this DC-microgrid energy system. The main purpose of this project is to determine which parts and processes of the DC-microgrid contribute to highest environmental impact throughout their lifespan from cradle to gate stages. Moreover, this study explores the energy payback time (EPBT) and the cumulative energy demand (CED) for the DC-microgrid. Additionally, this study performs two comparative LCA. First the DC-microgrid is being compared with PV system to determine which system has higher environment impacts, and secondly, the DC-microgrid is being compared with the average electricity mix in Sweden in terms of contribution to environmental impacts. The LCA follows the ISO 14040 framework and the baseline method is applied in order to assess 11 environmental impact categories. Two different functional units are adopted in this study. One is based on installed kilowatt peak (kWp) capacity by which environmental impacts of the PV system are compared with the DC-microgrid system. The other functional unit for this study is 1 kWh of delivered electricity to residential buildings produced by the DC-microgrid system. This functional unit is used exclusively for a stand-alone analysis of the DC-microgrid system in order to make it comparable with other microgrid systems or other systems with different energy sources, such as hydro, wind or nuclear. The results of the stand-alone LCA analysis of the DC-microgrid show that the battery has high contribution in human toxicity and terrestrial ecotoxicity whereas the energy hub system (Ehub) is the main contributor to eutrophication, abiotic depletion, fresh water aquatic ecotoxicity and marineaquatic ecotoxicity. The monocrystalline PV panel has the highest impact on global warming and abiotic depletion (fossil fuel). In addition, the EPBT for the DC-microgrid system is approximately 3.7 years. This means that one can get energy free of cost for an estimated time of 26.5 years if the lifetime of the system is assumed to be 30 years. The CED results show that monocrystalline PV production is an intense energy process which requires more non-renewable energy than all remaining parts of the DC-microgrid. The comparison of the DC-microgrid with the PV system reveals that the DC-microgrid has a higher environmental impact almost in all impact categories. This is mainly due to batteries and inverters which have a clear effect on the result. The CED analysis results illustrate that the multicrystalline PV panel production from the PV system is the most energy demanding process in both categories of renewable and non-renewable energy source. Moreover, the analysis illustrates that the DC-microgrid has still higher environmental impacts in all impact categories compared to the average electricity mix in Sweden. This is due to the electricity production in Sweden relies on hydropower and nuclear power with around 83 % of the total electricity production in the year 2017 which causes a lower environmental burden. Although the DC microgrid system shows a higher environmental impact compared to PV system, it is still a proper option to generate electricity since DC-microgrid system allows to achieve some indirect advantages such as energy saving due to an increase in own usage rate and self-sufficiency rate compared to the PV system. It should be noted that the end-of-life procedures becomes very important especially when crediting back for the recycling of materials. The collection and recycling of the PV panels at their end-of-life should be considered for future work as soon as reliable data are available. / Användningen av solpaneler har de senaste åren kommit att öka markant i Sverige. Ökningen beror på det statliga bidraget för installation av solceller som lanserades 2009. Kungsbacka kommun installerade solcellssystem i två olika typer av byggnader, ny och äldre befintlig byggnad. Den nya byggnaden anslöts till direkt mikronät (DC-mikcrogrid) och den äldre byggnaden utrustades med solcellssystem. Detta projekt utför en ’från vaggan till porten’ livscykelanalys (LCA) för energisystemet direkt mikronät. Syftet är i huvudsak att fastställa vilka delar och processer av det direkta mikronätet som bidrar till störst miljöpåverkan genom dess livslängd, det vill säga från vaggan till porten. Vidare undersöker studien återbetalningstiden (Energy PayBack Time, EPBT) och den ackumulerade energianvändningen (Cumulative Energy Demand, CED) för det direkta mikronätet. Studien utför två komparativa LCA varpå det direkta mikronätet först jämförs med solcellssystemet i syfte att fastställa vilket av systemen har större miljöpåverkan. Studien ämnar också jämföra det direkta mikronätet med den genomsnittliga energimixen i Sverige, också avseende miljöpåverkan. LCA metoden följer ISO 14040-ramverket. Studien är baserad på två funktionella enheter vilka består av installerad kilowatt peak (kWp) kapacitet vilken används för att jämföra solcellssystemet och det direkta mikromåttet. Den andra funktionella enheten är 1 kWh levererad elektricitet till bostäder som producerats genom det direkta mikronätet. Denna funktionella enhet används för en ’stand-alone’ analys av det direkta mikronätet i syfte att göra det jämförbart med andra mikrosystem eller system med olika energikällor så som vatten-, vind- och kärnkraft. Resultaten från ‘stand-alone’ livscykelanalysen av det direkta mikronätet visar på att batteriet har en större effekt på mänsklig toxicitet terrestrisk ekotoxicitet, varpå systemet för energihubb bidrar främst till övergödning, abiotisk utarmning, vattenlevande ekotoxicitet och havslevande ekotoxicitet. Monokristallin solpanel har större påverkan på global uppvärmning och övergödning (fossilabränslen). I övrigt är EPBT för det direkta mikronätet cirka 3,7 år vilket innebär att energin beräknas kostnadsfri i cirka 26,5 år, givet att det kan antas att systemets livslängd är 30 år. CED-resultat visar på att microkristallin solpanel är en intensiv energiprocess som kräver mer icke-förnybar energi jämfört med resterande delar av det direkta mikronätet. Jämförelsen mellan det direkta mikronätet och solcellssystemet visar på att det direkta mikronätet har större miljöpåverkan i de flesta kategorier. Detta beror i huvudsak på batterier och växelriktare som har tydlig effekt på resultatet. Av resultatet från CED-analysen framgår att produktion av multikristallin solpanel av solcellssystemet är det mest energikrävande processen i båda kategorierna för förnybar och icke-förnybar energikälla. Vidare framgår av analysen att det direkta mikronätet har en större miljöpåverkan i alla kategorier, jämfört med påverkan från genomsnittet av energimixen i Sverige. Detta beror på att elproduktionen i Sverige mestadels består av vatten- och kärnkraft som tillsammans 2017 utgjorde 83 procent av den totala energiproduktionen. Denna produktion orsakaren mindre miljöbelastning. Trots att det direkta mikronätet påvisar en högre miljöpåverkan än solcellssystemet, är det fortfarande ett alternativ till att generera elektricitet eftersom det direkta mikronätet bidrar till indirekta fördelar såsom energibesparing. Energibesparingen i det direkta mikronnätet sker således genom ökad användning av den egenproducerade energin samt självförsörjning. Det ska vidare tilläggas att ’end-of-life’ procedurerna blir viktiga i synnerhet när de återvunna materialet återanvänds. Vidare bör solpaneler återanvändas vid ’end-of-life’ vilket bör finnas i åtanke för vidarestudier och i samband med att data tillgängliggörs.

Detta projekt har haft målet att designa en regulator som ska hålla en viss frekvens och spänning i ett microgrid under olika steg. Dessa var att koppla bort microgridet från huvudnätet, justera fasvinkeln så att den överensstämmer med huvudnätets och till sist återansluta näten. Fyra olika scenarier genomfördes. Första var utan regulatorn, två stycken skedde med olika last och den sista med förnybara energikällor. En PI-regulator implementerades i en Raspberry PI vilken var kopplad till microgridet. Resultaten visade att första scenariot ledde till instabilitet i microgridet. När regulatorn installerades kunde hela simulationen genomföras med undantag från en något hög översläng på spänningen i scenariot med förnybara energikällor. Förutom detta uppnåddes målet och regulatorn fungerade för det använda microgridet. För att få mindre översläng på spänningen med förnybara energikällor skulle PI-regulatorn behöva justeras något. / This project aimed to design a controller for a microgrid that would maintain a desired frequency and voltage throughout several steps. These were disconnecting the microgrid to islanded mode, shifting the phase angle with respect to the main grid and finally reconnecting the grids. Four different scenarios were executed. The first one without the controller, two with different loads and one with renewable energy. A PIregulator was implemented in a Raspberry PI which served as controller. The results showed that the first scenario without a controller ended up with instability. Once the PI-regulator was installed the full simulation could be completed properly, except slightly too high overshoot on the voltage with renewable energy sources installed. Consequently the goal was achieved and the controller performed the requested tasks. Regarding the scenario with renewable energy the controller would need some adjustments to regulate the voltage properly.

Smart grid is more than just the smart meters. The future smart grids are expected to include a high penetration of distributed generations (DGs), most of which will consist of renewable energy sources, such as solar or wind energy. It is believed that the high penetration of DGs will result in the reduction of power losses, voltage profile improvement, meeting future load demand, and optimizing the use of non-conventional energy sources. However, more serious problems will arise if a decent control mechanism is not exploited. An improperly managed high PV penetration may cause voltage profile disturbance, conflict with conventional network protection devices, interfere with transformer tap changers, and as a result, cause network instability. Indeed, it is feasible to organize DGs in a microgrid structure which will be connected to the main grid through a point of common coupling (PCC). Microgrids are natural innovation zones for the smart grid because of their scalability and flexibility. A proper organization and control of the interaction between the microgrid and the smartgrid is a challenge. Cooperative control makes it possible to organize different agents in a networked system to act as a group and realize the designated objectives. Cooperative control has been already applied to the autonomous vehicles and this work investigates its application in controlling the DGs in a micro grid. The microgrid power objectives are set by a higher level control and the application of the cooperative control makes it possible for the DGs to utilize a low bandwidth communication network and realize the objectives. Initially, the basics of the application of the DGs cooperative control are formulated. This includes organizing all the DGs of a microgrid to satisfy an active and a reactive power objective. Then, the cooperative control is further developed by the introduction of clustering DGs into several groups to satisfy multiple power objectives. Then, the cooperative distribution optimization is introduced iii to optimally dispatch the reactive power of the DGs to realize a unified microgrid voltage profile and minimize the losses. This distributed optimization is a gradient based technique and it is shown that when the communication is down, it reduces to a form of droop. However, this gradient based droop exhibits a superior performance in the transient response, by eliminating the overshoots caused by the conventional droop. Meanwhile, the interaction between each microgrid and the main grid can be formulated as a Stackelberg game. The main grid as the leader, by offering proper energy price to the micro grid, minimizes its cost and secures the power. This not only optimizes the economical interests of both sides, the microgrids and the main grid, but also yields an improved power flow and shaves the peak power. As such, a smartgrid may treat microgrids as individually dispatchable loads or generators.

Over the last decade, microgrid deployments throughout the world have increased. In 2019, a record number of 546 microgrids were installed in the United States [1]. This trend continues upward to combat extreme weather conditions and power shortages throughout the country. To better equip students with the necessary skillsets and knowledge to advance in the microgrid field, Cal Poly San Luis Obispo's Electrical Engineering Department and the Power Energy Institute have invested resources to develop a laboratory microgrid. This thesis sets to improve the laboratory microgrid's existing automation using the Schweitzer Engineering Laboratory SEL-3530 Real-time Automation Controller (RTAC). The improved automation features a new load-shedding scheme, LCD annunciator and meter panel, and emergency safety shutdown system. The load shedding scheme aims to enhance the grid's frequency stability when the inverter-based power output declines. The LCD annunciator and meter panels provide real-time oversight of the microgrid operating conditions via the RTAC Human Machine Interface (HMI). The emergency safety shutdown enables prompt de-energization and complete isolation of the laboratory microgrid in hazardous conditions such as earthquake, fire, arcing, and equipment malfunction and activates an audible siren to alert help. This safety system provides safety and peace of mind for students and faculties who operate the Microgrid. Lastly, this thesis provides an operating procedure for ease of operation and experiment.

A microgrid is a local energy grid that could be disconnected from the larger grid and operate autonomously. This particular segment of the power industry is growing due to its reliability during times of emergency and crisis. Among these benefits are improved efficiency, lower operating costs, renewable generation sources, and improved resiliency of the regional electric grid. Communities can better prepare for unprecedented weather like wildfires, hurricanes, and other natural disasters. Regions that produce renewable energy can export their surplus through high voltage transmission lines to balance power supply and demand needs. This Underground High-Voltage Transmission Network project aims to design a blueprint for an underground high voltage transmission network that connects the Cal Poly Solar Farm to campus via an underground network. This Solar Farm produces 4.5MW and provides a quarter of Cal Poly’s power demand, making it essential to everyday operations on campus. The safety of the communities living around these areas is a top priority. The project develops methods to examine network resiliency and analyze load growth or demography trends. These methods include: using GIS to properly locate any existing underground infrastructure and utilizing CYMCAP software to size cable. We use ETAP software to run load flow analysis and device coordination simulation.

Yes / Hybrid AC-DC microgrid (HMG) allows direct integration of both AC distributed generators (DGs) and DC DGs, AC and DC loads into the grid. The AC and DC sources, loads are separate out and are connected to respective subgrid mainly to reduce the power conversion, thus the overall efficiency of the system increases. This paper aims to introduce a novel hybrid AC-DC microgrid planning and design model within a microgrid market environment to maximize net social welfare (NSW). NSW is defined as present value of total demand payment minus present value of total planning cost including investment cost of distributed energy sources (DERs) and converters, operation cost of DERs and the cost of energy exchange with the utility grid subject to network constraints. Scenario Tree approach is used to model the uncertainties related to load demand, wind speed and solar irradiation. The effectiveness of the proposed model is validated through the simulation studies on a 28-bus real hybrid AC-DC microgrid.

Yes / Daily increasing use of tidal power generation proves its outstanding features as a renewable source. Due to environmental concerns, tidal current energy which has no greenhouse emission attracted researchers’ attention in the last decade. Additionally, the significant potential of tidal technologies to economically benefit the utility in long-term periods is substantial. Tidal energy can be highly forecasted based on short-time given data and hence it will be a reliable renewable resource which can be fitted into power systems. In this paper, investigations of effects of a practical stream tidal turbine in Lake Saroma in the eastern area of Hokkaido, Japan, allocated in a real microgrid (MG), is considered in order to solve an environmental/economic bi-objective optimization problem. For this purpose, an intelligent evolutionary multi-objective modified bird mating optimizer (MMOBMO) algorithm is proposed. Additionally, a detailed economic model of storage devices is considered in the problem. Results show the efficiency of the suggested algorithm in satisfying economic/environmental objectives. The effectiveness of the proposed approach is validated by making comparison with original BMO and PSO on a practical MG. / Iran National Science Foundation; Royal Academy of Engineering Distinguished Visiting Fellowship under Grant DVF1617\6\45

This dissertation presents a comprehensive investigation into the performance optimization of a smart energy system called the Integrated Community Energy and Harvesting (ICE-Harvest) system, designed to optimize energy utilization in dense communities in cold climates. This system comprises a single-pipe variable-temperature micro-thermal network, a micro-electrical network, and distributed energy resources such as combined heat and power units, boilers, heat pumps, short-term storage systems, and long-term storage system. The objective of this research is to develop an optimal operation strategy for the system, considering the coordination of its components to realize its full potential including achieving demand management while ensuring occupants' comfort, harvesting and sharing waste energy, and facilitating energy arbitrage and taking advantage of energy price fluctuations, among other benefits. For this aim, the study begins by formulating precise quasi-dynamic mathematical representations of the system, considering the physical and operational limitations to capture the system's intricacies. The resultant optimization problem is a mixed integer nonlinear programming model that commercial solvers could not solve. To make the nonlinear models more tractable and solvable, various mathematical techniques are employed to linearize them. It is worth noting that many of these formulations are original contributions to the field. Given the specific configuration of the system with components requiring short-term and long-term operation scheduling and the large-scale nature of the optimization problem, a decomposition algorithm is proposed that breaks down the problem into three sequential layers: long-term, short-term, and ultra-short-term. Each layer addresses specific planning horizons, time resolutions, and optimization models, enabling effective optimization of the system's operation. The proposed optimization algorithm offers an effective framework for planning and optimizing ICE-Harvest operation at various time horizons and resolutions. It demonstrates the system's flexibility in performing waste energy harvesting and sharing, demand management, and dynamic switching between energy carriers based on real-time prices. / Dissertation / Doctor of Philosophy (PhD) / This dissertation aims to develop an energy management system for an integrated smart energy system, called integrated community energy and harvesting (ICE-Harvest). The ICE-Harvest system is envisioned as the future of energy systems for dense com munities in cold climates. This system comprises a single-pipe variable-temperature micro-thermal network, a micro-electrical network, and distributed energy resources. The goal is to coordinate all the variables and assets so that the system’s capabilities in harvesting waste energy to offset the community’s thermal demands, performing demand management without affecting occupants’ comfort, and realizing energy arbi trage are realized. For this aim, a hierarchical decision-making framework is developed in which three sequential layers are integrated. The three layers determine the long term, short-term, and ultra-short-term optimal operation of the ICE-Harvest system. The layers are differentiated by their objective, planning horizon, time resolution, and optimization models.