Sweden has developed ambitious goals regarding energy and climate politics. One major goal is to change the entire electricity production from fossil fuels to sustainable energy sources, this will contribute to Sweden being one of the first countries in the world with non-fossil fuel in the electricity sector. To manage this, major changes need to be implemented and difficulties on the existing grid will occur with the expansion of digitalization, electrification and urbanization. By using smart grids, it is possible to deal with these problems and change the existing electricity grid to use more distributed power generation, contributing to flexibility, stability and controllability. The goal with smart grids is to have a sustainable electricity grid with low losses, security of supply, environmental-friendly generation and also have choices and affordable electricity for customers. The purpose of this project is to identify and evaluate several indicators for a smart grid, how they relate and are affected when different scenarios with different technologies are implemented in a test system. Smart grid indicators are quantified metrics that measure the smartness of an electrical grid. There are five scenarios where all are based on possible changes in the society and electricity consumption, these scenarios are; Scenario A – Solar power integration, Scenario B – Energy storage integration, Scenario C – Electric vehicles integration, Scenario D – Demand response and Scenario E – Solar power, Energy storage, Electric vehicles and Demand response integration. A model is implemented in MATLAB and with Monte Carlo simulations expected values, standard deviation and confidence interval were gained. Four selected indicators (Efficiency, capacity factor, load factor and relative utilization) was then analyzed. The results show that progress on indicators related to all smart grid characteristics is needed for the successful development of a smart grid. In scenario C, all four selected indicators improved. This shows that these indicators could be useful for promoting the integration of electric vehicles in an electricity grid. In Scenario A, solar power integration contributed to all indicators deteriorate, this means that, technical solutions that can stabilize the grid are necessary to implement when integrating photovoltaic systems. The load factor is a good indicator for evaluating smart grids. This indicator can incentivize for an even load and minimize the peak loads which contributes to a flexible and efficient grid. With the capacity factor, the utilization and free capacity can be measured in the grid, but it can counteract renewable energy integration if the indicator is used in regulation.

The Master Thesis is part of the Smart Grid Gotlandproject. This project aims at implementing smart grid solutionson the island of Gotland in order to be able to efficientlyintegrate large quantities of renewable energy production.In situations of high wind power production and lowconsumption, energy export problems may occur betweenGotland and the mainland. A novel approach to manageanticipated congestions, compared to traditional gridreinforcements, consists of using flexibility from demandresponse(DR) resources. However, such an approach presentschallenges as it requires both technical and economic considerations.This Master Thesis proposes and analyses twomarket-based strategies applied to detached houses for dayaheadcongestion management. The strategies are implementedin an Ancillary Service toolbox developed in theMATLAB programming environment.The first strategy involves using a dynamic network tariffwhile the second uses spot price optimization. Simulationsare performed for seasonal worst-case congestion scenarioswhile satisfying comfort and economic constraints ofthe DR participants. A sensitivity analysis is carried out toassess the impact of different spot price profiles and windpower production prognosis errors on the results.Results show that congestions are managed with a feasiblenumber of participants, but that their savings are negligiblefor both strategies (between 2 and 40 SEK/participant).Moreover, using a dynamic network tariff strategy impliesa DSO cost in the range of 1700-89000 SEK. These resultsapply for a 3-days congestion period, which is estimated tooccur 5-6 times a year if the maximum hosting capacity isincreased by 5 MW.To conclude, an AS toolbox with economic constraintsis feasible for Gotland conditions with a reasonable numberof DR participants. However, the simple cost-benefitanalysis that was carried out showed that the AS toolboxapproach was still much more costly than traditional gridreinforcement.

According to the 20-20-20 targets set by the European Union, 50 percent of the Swedish electricity share is to be provided by renewable energy sources by 2020. The Smart Grid Gotland (SGG) project has emerged as a response to this target. The project aims at demonstrating a proof of concept on how smart grid solutions can be used to integrate large quantities of renewable energy sources in an existing network. The outcomes of the project are intended to pave the way for future renewable energy integration projects in Sweden. The Thesis focuses on one of the technical objectives of the SGG project, i.e. to increase the hosting capacity of wind power on Gotland from 195 MW to 200 MW by using Demand-Response (DR) from households and industries. DR consist of shifting peak-loads to peakproduction hours. The integration of additional wind power causes a risk of exceeding the transmission capacity of the power export cable between Gotland and the Swedish mainland. The approach considered for this Thesis is to use an Ancillary Service (AS) toolbox scheme based on multi-agent systems. The AS toolbox consist of flexibility tools such as DR on long-term, short-term, a battery energy storage system and a wind curtailment scheme. The DR activity includes space heating and domestic hot water consumption from detached houses on Gotland. The simulation results indicate that 1900 household participants are sufficient to balance the additional 5 MW for worst case scenarios. Furthermore, it is shown that the DR participation from industries contributes in some cases to a reduction of 700 household participants. The findings helped conclude that using an AS toolbox solution on Gotland is fully possible from a technical perspective. However, barriers that stand against its realisation are of economical nature and need to be investigated in future studies.

I Sverige står hushållen för en stor del av den slutliga elanvändningen och deras konsumtionsmönster bidrar till att skapa höga förbrukningstoppar, särskilt under vintermånaderna när elbehovet är som störst. Om hushållen kunde tänka sig att vara mer flexibla i när de använder el skulle förbrukningstoppar kunna dämpas avsevärt och balansen mellan elproduktion och elanvändning bli lättare att upprätthålla. Idag utnyttjas inte efterfrågeflexibilitet i någon större utsträckning, förutom den från vissa elintensiva industrier. Den flexibla kapacitet enskilda hushåll skulle kunna bidra med är naturligtvis lägre än hos industrier, men sammanslaget skulle hushållskunders flexibilitet kunna ge en substantiell inverkan på elsystemet. Vid låga utetemperaturer finns det en uppskattad potential att genomföra effektjusteringar om cirka 1 400 – 3 100 MW om värmelasten hos drygt hälften av samtliga eluppvärmda hus i Sverige omdisponeras till andra tidpunkter, och ytterligare några hundra MW om drygt hälften av samtliga hushåll i Sverige vore flexibla i när de använder hushållsel. Enligt en studie av Broberg m.fl. (2016) skulle drygt hälften av hushållen i Sverige kunna tänka sig att vara flexibla, beroende på vad flexibel innebär. Hushåll som använder el för uppvärmning kan vara flexibla genom att tillfälligt öka eller minska inomhustemperaturen, eller om de använder el i kombination med något annat uppvärmningssätt – genom att byta energikälla. Justeringen kan även ske automatiskt om uppvärmningssystemet är utrustat med central styrutrustning. Om ett stort antal kunders flexibla laster samlas ihop av en marknadsaktör skulle den totala flexibla lasten kunna säljas som kapacitet på grossistmarknaden för el eller erbjudas som upp- eller nedregleringsbud på reglermarknaden. Studien av Broberg m.fl. (2014) har även analyserat hur stor ekonomisk kompensation hushåll vill ha i utbyte mot att vara flexibla. Sett till den flexibla kapacitet hushållskunders efterfrågeflexibilitet bedöms motsvara, cirka 1 400 – 3 100 MW, är kompensationskraven legitimerade, åtminstone om den flexibla kapaciteten erbjuds på någon marknadsplats för elhandel. Styrtjänster som innebär att elanvändningen automatiskt optimeras efter elpriset kan dock vara dyra idag, vilket innebär att det främst tros vara hushåll med hög elförbrukning som utnyttjar dem och de bör därför subventioneras. En annan form av flexibilitet är att anpassa elanvändningen efter det timvarierande elhandelspriset. Den enda förutsättningen för att konsumenten ska tjäna på en sådan anpassning är att elförbrukningen mäts och debiteras på timbasis, vilket är fallet för de relativt få kunder som har valt att teckna timprisavtal. På grund av att konsumentpriset på el inte varierar särskilt mycket saknas incitament för att kunder ska vilja anpassa sin användning efter priset. Det behövs därför en mer effektiv prissättning som exempelvis förstärker volatiliteten eller gör det dyrare eller billigare att använda el vid vissa tidpunkter. / In Sweden, residential consumers account for a large share of the final electricity consumption. Their consumption patterns pose great impact on the network power peaks, especially during the winter. If residential consumers were more flexible in their consumption, peaks would be alleviated considerably and the balance between electricity supply and demand would more easily be maintained. Today, demand side flexibility is not utilized to any greater extent, except the one from energy intensive industries. De flexible capacity a single household could contribute with is of course less than within industries, but if flexible capacity from a large number of households were bundled up it would provide a considerable impact on the electricity system. At low outdoor temperatures there is an estimated potential to reach power adjustments in the size of 1 400 – 3 100 MW if the heat load in just over half of the electric heated houses in Sweden were displaced, and a few hundred more if residential consumers were flexible in their consumption of domestic electricity. According to a study by Broberg et al (2016) approximately half the population would consider to be flexible in their electricity consumption under the right circumstances. Households that use electricity for heating can be flexible through temporarily adjust the indoor temperature, or – if they heat their homes with electricity in combination with another heat source – by switching heat source. The adjustment can also be automatic if the heating system is equipped with a central control unit. If flexible capacity from a large number of households is bundled up into grid worthy demand response by a market actor, the capacity could be offered as bids on organized electricity markets. The study by Broberg et al (2014) also analysed how much compensation households require in exchange for being flexible. The compensation levels are justified with regard to the flexible capacity that can be gathered form households, 1 400 – 3 100 MW, at least as long as the capacity is sold in an organized electricity market. Services for automatic control of heating systems, where the power output is optimized after the varying electricity price, can be expensive today, which indicates that mainly households with a high electricity consumption utilize them today. Hence, they need to be subsidized. Demand side flexibility can also be to manually change consumption patterns in response to price signals. The only precondition is that the electricity consumption is measured and billed on an hourly basis, which is the case for the relatively few consumers with hourly rate agreements. The volatility of the electricity price is however subdued due to the large share of fixed surcharges, which means there is lack of incentive for consumers to adapt their consumption in response to price variations. Hence, the volatility needs to be amplified through efficient pricing.

Under the determined impulse of the European Union to limit the environmental impact of energy-related services, the electricity sector will face several challenges in coming years. Integrating renewable energy sources in the distribution networks is certainly one of the most urging issues to be tackled with. The current grid and production structure cannot absorb the high penetration shares anticipated for 2020 without putting at risk the entire system. The innovative concept of smart grid offers promising solutions and interesting implementation possibilities. The objective of the thesis is to specifically study the technical and economic benefits that the creation of an aggregator on the Swedish island of Gotland would imply. Comparing Gotland's power system characteristics to the broad variety of solutions offered by demand side management, wind power integration enhancement by demand response appeared particularly suited. A business case, specifically oriented towards the minimisation of transmission losses by adapting the electric heat load of private households to the local wind production was designed. Numerical simulations have been conducted, evaluating the technical and economic outcomes, along with the environmental benets, under the current conditions on Gotland. Sensitivity analyses were also performed to determine the key parameters for a successful implementation. A prospective scenario for 2020, with the addition of electric vehicles, has finally been simulated to estimate the long term profitability of an aggregator on the island. The simulation results indicate that despite patent technical benefits for the distribution network, the studied service would not be profitable in the current situation on Gotland. This, because the transmission losses through the HVDC-cable concern limited amounts of power that are purchased on a market characterized by relatively cheap prices and low volatility. Besides, the high fixed costs the aggregator has to face to install technical equipment in every household constitutes another barrier to its setting up.

L’Internet des Objets (IdO) et le déploiement des équipements connectés permettent la mise en place de solutions de Gestion Active de la Demande(GAD) avancées. En effet, il devient possible d’avoir plus de visibilité et un contrôle fin sur différents équipements qui consomment, stockent ou produisent de l’énergie dans une maison. Dans cette thèse, nous considérons des solutions ayant la capacité de produire des décisions de contrôle direct à différents niveaux de granularité en fonction des variables mesurées dans les habitats. Le contrôle est basé sur une optimisation d’utilité perçue. Des fonctions utilité sont définies à travers une approche générique qui considère la flexibilité de la charge et l’impact des décisions de contrôle sur les utilisateurs. L’approche proposée n’impose pas de restrictions sur le type des équipements contrôlés ni sur la granularité des décisions de contrôle. Ceci permet un contrôle joint d’équipements hétérogènes. Nous considérons trois types d’architectures de contrôle à savoir: des solutions centralisées, partiellement distribuées et entièrement distribuées. Ces architectures diffèrent dans la distribution de la prise de décision entre les entités impliquées dans le contrôle et les données qui sont mis à disposition de ces entités. L’analyse numérique montre les compromis des solutions proposées du point de vue de la performance, de l’extensibilité et de la complexité. / The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. Indeed, it enables visibility and control on the various appliances that may consume, store or generate energy within a home. In this thesis, we consider solutions having the capability to produce direct control decisions at different granularities based on variables measured at homes. Control schemes are driven by an optimization based on utility functions. These functions are defined based on a generic approach that considers load’s flexibility and the impact of control decisions on users. The proposed approach does not impose any restrictions on the type of controlled appliances nor on the granularity of control decisions. This enables joint control of heterogeneous loads. We consider three types of control architectures, namely centralized, partially distributed and fully distributed solutions. Schemes based on these architectures differ in the distribution of decision making among entities involved in the control and data that is made available to these entities. Numerical analysis shows the trade-offs of proposed solutions from a performance, scalability and complexity perspectives.

The power grid faces major and escalating challenges in maintaining the power balance whilst society transitions towards increased sustainability. One promising solution to this challenge is found in the concept of demand response, where consumers adapt their energy demand due to some incentive in order to help balance the power grid. This study analyses the technical potential for industrial energy consumers to provide demand response by combining theory on demand response with theory on operations management and puts this to the test through a case study of a Swedish industrial sheet metal plant. In the study relevant factors such as energy and productivity parameters as well as planning and business models are shown to restrict the demand response potential. Different kinds of load shape objectives are analyzed, where peak clipping is shown to be simple but costly whilst load shifting is shown to be more complex but with the potential of offering demand response without affecting the overall productivity of the plant. These results help expand the picture of industrial consumer demand reponse from a static value depending on the economical incentive into a more complex concept that requires further research and optimization.

Integrating the electrical and thermal community buildings' energy systems can play an important role in harvesting wasted energy resources and reduction of carbon emissions from buildings and electricity generation sectors. It also increases demand management flexibility by minimizing the curtailed electricity on the grid through electrified heating without increasing the electricity peak demand. The current work examines Integrated Community Energy and Harvesting systems (ICE-Harvest), a new generation of distributed energy resources systems (DERs). They prioritize the harvesting of community waste energy resources—for example, heat rejected from cooling processes and distributed peak electricity fossil-fuel-fired generators, as well as energy from curtailed clean grid electricity resources—to help in satisfying the heating demands of commercial and residential buildings. As such, ICE-Harvest systems provide a solution that can minimize greenhouse gas emissions from high-energy-consumption buildings in cold-climate regions such as North America and Northern Europe. In the current research, a thermal energy sharing model was developed to provide a dynamic characterization of the potential benefits of integrating and harvesting energy within a community of any number of buildings. The proposed model estimates the amount of rejected heat from cooling and refrigeration systems that can be simultaneously collected and used to heat other nearby buildings connected with a low temperature microthermal network (MTN). It also determines the proper timing and quantity of electricity used by the heat pumps in low-temperature MTNs as well as the reduction of both GHG emissions and the energy required from the EMC relative to conventional stand-alone systems. For an energy-balanced community cluster, the model showed that, over the course of a year, the energy harvesting would reduce this node’s GHG emissions by 74% and cover approximately 82% of the heating requirements compared to the BAU system. The results also revealed that the diversity in thermal demand between the connected buildings increases the harvesting potential. This research develops two clustering methods for the ICE-Harvest system. The proposed methods are clustering around anchor building and density-based (DB) clustering with post-processing by adding the closest anchor building to each cluster that focuses on the diversity of the buildings in each cluster. The energy sharing model is used to examine these techniques in comparison with the density-based clustering technique, the commonly used technique in the literature on a large database of 14000 high energy consumption buildings collected in Ontario, Canada. The results of this case study reveal that DB clustering with post-processing resulted in the largest emission reduction per unit piping network length of 360 t CO2eq /km/year. In addition, this research identified seven different cluster categories based on the total and simultaneous cooling-to-heating ratios of each cluster. The ICE harvest system integrates the thermal and electrical networks to add more flexibility to the electricity grid and schedule the electrification of heating (EoH). Current research provides a reduced model for the ICE-Harvest system to study its impact for over 1100 clusters of different categories on a provincial scale on the GHG emission and electricity demand from the grid. The use of ICE-Harvest systems at this scale can displace the energy required from the gas-fired heating resources by 11 TWh, accounting for over 70% of the clusters’ total heating requirements. This results in a 1.9 Mt CO2eq reduction in total GHG emissions, which represents around 60% of the clusters’ emissions. Operating conditions of the thermal network (TN) in the integrated community energy systems affect the ability to harvest waste energy and the reduction of GHG emissions as well as the electricity peak demand and consumption. In the current research, modeling of different thermal distribution network operating scenarios was performed for the different community energy profile clusters. These operation scenarios include low-temperature (fourth generation), ultra-low (fifth generation), a binary range-controlled temperature modulating thermal network operating between Low and Ultra-low temperatures (ICE-Harvest), and a new proposed scenario wherein a continuous range-controlled temperature modulating micro-thermal network. The continuous range-controlled temperature scenario shows the most benefits with the large implementation on the identified clusters. It adds more flexibility to balance the electricity grid as well as results in large GHG emission savings while controlling the increase in site electricity peak demand. The load profile of the cluster affects the selection of the most beneficial energy integrated system. This research shows that, for most of the heating-dominated clusters, it is better to employ the continuous range-controlled temperature TN with peak control and CHP on sites to serve the high heating demands along with short term and seasonal thermal storage. For the majority of balanced and /or cooling-dominated clusters, it is better to implement more carbon-free resources to the electricity grid or on-site that produce electricity but are not associated with heat such as wind, hydro, and solar PV panels. Parametric studies were performed in this research including changing the CHP size, the CHP utilization efficiency, and the grid gas-fired generators usage conditions to show their impact on the GHG emissions reduction from the clustered buildings. The analysis was implemented on a fleet of 1139 sites in Ontario and the results showed that the CHP size and operating hours have a measurable impact on GHG emission saving. The system can reach up to 58% and 66.5% emission savings of the total sites’ emissions with 93% and 39% operating hours respectively following the Ontario grid natural gas peaking power plants for the years of 2016 and 2017 with larger CHP sizes. The largest share of GHG emission saving in 2016 is by the CHP (61%) as opposed to 30% in 2017. The reduced models introduced in this research for the thermal energy sharing, the ICE-harvest system operation and sizing, and the MTN operation aid the investigation of the impact of the large implementation of the ICE-Harvest systems on the GHG emissions and electricity grid. / Thesis / Doctor of Philosophy (PhD)

Yes / This article proposes a novel technique for operation of distribution networks with considering active network management (ANM) schemes and demand response (DR) within a joint active and reactive distribution market environment. The objective of the proposed model is to maximize social welfare using market-based joint active and reactive optimal power flow. First, the intermittent behavior of renewable sources (solar irradiance, wind speed) and load demands is modeled through scenario-tree technique. Then, a network frame is recast using mixed-integer linear programming, which is solvable using efficient off-the-shelf branch-and cut solvers. Additionaly, this article explores the impact of wind and solar power penetration on the active and reactive distribution locational prices within the distribution market environment with integration of ANM schemes and DR. A realistic case study (16-bus UK generic medium voltage distribution system) is used to demonstrate the effectiveness of the proposed method. / This work was supported in part by the Ministry of Higher Education Scientific Research in Iraq and in part by British Academy under Grant GCRFNGR3\1541.

The concept of positive energy districts (PED) encompasses a range of policies and strategies in response to climate protection targets in urban areas. Due to the limited potential of renewable energy in urban neighborhoods, broader definitions of PED are proposed that allow for energy exchange through the grid infrastructure. This study evaluates demand side management in combination with a virtual power plant (VPP) to assess the impact on the design of PED. In particular, the optimal customer behavior in response to flexible electricity tariffs is analyzed. A techno-economic energy system model is proposed for an urban area in Germany that optimizes the customer cost and the VPP’s margin. This includes electrical energy generation, storage, demand, and access to the short-term electricity market. Based on economic analysis, a dynamic market-based tariff allows the VPP to maximize profit margins. Consumers benefit when the local balances of renewable energy supply and demand are integrated into the dynamic tariff.