As global climate change concerns, technological advancements, and economic shifts increase the adoption of electric vehicles, it is vital to study how best to integrate these into our existing energy systems. Electric vehicles (EVs) are on track to quickly become a large factor in the energy grid. If left uncoordinated, the charging of EVs will become a burden on the grid by increasing peak demand and overloading transformers. However, with proper charging control strategies, the problems can be mitigated without the need for expensive capital investments. Distributed control methods are a powerful tool to coordinate the charging, but it will be important to assess the trade-offs between performance, information privacy, and computational speed between different control strategies. This work presents a comprehensive comparison between four distributed control algorithms simulating two case studies constrained by dynamic transformer temperature and current limits. The transformer temperature dynamics are inherently nonlinear and this implementation is contrasted with a piece-wise linear convex relaxation. The more commonly distributed control methods of Dual Decomposition and Alternating Direction Method of Multipliers (ADMM) are compared against a relatively new algorithm, Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN), as well as against a low-information packetized energy management control scheme (PEM). These algorithms are implemented with a receding horizon in two distinct case studies: a local neighborhood scenario with EVs at each network node and a hub scenario where each node represents a collection of EVs. Finally, these simulation results are compared and analyzed to assess the methods’ performance, privacy, and processing metrics for each case study as no algorithm is found to be optimal for all applications.

Several demand side management (DSM) techniques and algorithms have been used in the literature. These algorithms show that by adopting DSM and Time-of-Use (TOU) price tariffs; electricity cost significantly decreases, and optimal load scheduling is achieved. However, the purpose of the DSM is to not only lower the electricity cost, but also to avoid the peak load even if the electricity prices low. To address this concern, this dissertation starts with a brief literature review on the existing DSM algorithms and schemes. These algorithms can be suitable for Direct Load Control (DLC) schemes, Demand Response (DR), and load scheduling strategies. \end{abstract} Secondly, the dissertations compares two of DSM algorithms to show the performance based on cost minimization, voltage fluctuation, and system power loss [see in Chapter 5]. The results show the importance of balance between objectives such as electricity cost minimization, peak load occurrence, and voltage fluctuation evolution while simultaneously optimizing the cost.

La modernisation des réseaux électriques via ce que l'appelle aujourd'hui les réseaux intelligents (ou smart grids) promet des avancées pour permettre de faire face à une augmentation de la demande mondiale ainsi que pour faciliter l'intégration des ressources décentralisées. Grâce à des moyens de communication et de calcul avancés, les smart grids offrent de nouvelles possibilités pour la gestion des ressources des consommateurs finaux, y compris pour de petits éléments comme de l'électroménager. Cependant, ce type de gestion basée sur des décisions prises indépendamment peuvent causer des perturbations tels qu'un rebond de consommation, ou des instabilités sur le réseau. La prise en compte des interactions entre les décisions de gestion énergétique de différentes maisons intelligentes est donc une problématique naissante dans les smart grids. Cette thèse vise à évaluer l'impact potentiel de mécanismes de coordination entre consommateurs résidentiels au niveau de quartiers, et ce à travers trois études complémentaires. Tout d'abord, une première stratégie pour la gestion coordonnée de maisons est proposée avec l'objectif d'augmenter l'utilisation locale d'énergie renouvelable à travers la mise en place d'échanges d'énergie électrique entre voisins. Les participants reçoivent en échange une compensation financière. L'algorithme de gestion est étudié dans une configuration centralisée et une configuration décentralisée en faisant appel au concept de système multi-agents, chaque maison étant représentée par un agent. Les résultats de simulation montrent que les deux approches sont efficaces pour augmenter la consommation locale d'énergie renouvelable et réduire les coûts énergétiques journaliers des consommateurs. Bien que l'approche décentralisée retourne des résultats plus rapidement, l'approche centralisée a une meilleure performance concernant les coûts. Dans une seconde étude, deux algorithmes de gestion énergétiques à J-1 sont proposés pour un quartier résidentiel. Un modèle de tarification dynamique est utilisé, où le prix dépend de la consommation agrégée du quartier ainsi que d'une forme de tarification heures creuses-heures pleines. L'objectif est ici de concevoir un mécanisme de coordination plus avancé (par rapport au précédent), en permettant des échanges d'énergie renouvelable résiduelle au sein du quartier. La performance des algorithmes est étudiée sur une période d'une journée puis d'une année, en prenant ou non en compte les erreurs de prévision. D'après les résultats de simulation, les deux algorithmes proposés montrent de meilleurs performances que les méthodes de référence (sans contrôle, et algorithme égoïste), même en considérant les erreurs de prévision. Enfin, dans une troisième étude, l'impact de l'introduction de production photovoltaïque résidentielle sur la performance d'un agrégateur est évaluée, dans une configuration centralisée. L'agrégateur interagit avec le marché spot et le gestionnaire de réseau, de façon à proposer un nouveau modèle de tarification permettant d'influencer les consommateurs à agir sur leur consommation. Les résultats de simulation montrent quand le taux de pénétration de photovoltaïque résidentiel augmente, le profit de l'agrégateur diminue, du fait de l'autoconsommation dans le quartier. / Grid modernization through philosophies as the Smart Grid has the potential to help meet the expected world increasing demand and integrate new distributed generation resources at the same time. Using advanced communication and computing capabilities, the Smart Grid offers a new avenue of controlling end-user assets, including small units such as home appliances. However, with such strategies, decisions taken independently can cause undesired effects such as rebound peaks, contingencies, and instabilities in the network. Therefore, the interaction between the energy management actions of multiple smart homes is a challenging issue in the Smart Grid. Under this purpose, in this work, the potential of coordination mechanisms established among residential customers at the neighborhood level is evaluated through three studies. Firstly, coordinative home energy management is presented, with the aim to increase local renewable energy usage in the neighborhood area by establishing energy trading among smart homes, which are compensated by incentives. The control algorithm is realized in both centralized and decentralized manners by deploying a multi-agent system, where neighborhood entities are modeled as agents. Simulations results show that both methods are effective on increasing local renewable energy usage and decreasing the daily electricity bills of customers. However, while the decentralized approach gives results in shorter time, the centralized approach shows a better performance regarding costs. Secondly, two decentralized energy management algorithms are proposed for day-ahead energy management in the neighborhood area. A dynamic pricing model is used, where price is associated to the aggregated consumption and grid time-of-use scheme. The objective of the study is to establish a more advanced coordination mechanism (compared to previous work) with residual renewable energy is shared among smart homes. In this study, the performance of the algorithms is investigated with daily and annual analyses, with and without considering forecasting errors. According to simulations results, both coordinative control models show better performance compared to baseline and selfish (no coordination) control cases, even when considering forecasting errors. Lastly, the impact of photovoltaic systems on a residential aggregator performance (in a centralized approach) is investigated in a neighborhood area. In the proposed model, the aggregator interacts with the spot market and the utility, and proposes a novel pricing scheme to influence customers to control their loads. Simulation results show that when the penetration level of residential photovoltaics (PV) is increased, the aggregator profit decreases due to self-consumption ability with PV in the neighborhood.

The current state of Ontario’s electricity system and natural environment has prompted the provincial government to call for the province to adopt a ‘culture of conservation.’ Answering this call will involve the promotion of a variety of solutions. Included in that will be the use of information and communication technology, which encompasses technologies such as home energy management system (HEMS). It is believed that the feedback and home automation features of the HEMS will enable its users to alter their electricity consumption behaviours, via net reductions and/or load shifting. This study has assessed the ability of HEMS to encourage reduction in total and on-peak electricity consumption while in a time-of-use pricing environment. Additional focus was on which consumers had the greatest success using the HEMS to adopt electricity conservation behaviours. Two hundred and sixteen participants of a Milton, Ontario HEMS pilot study were chosen to take part in this case study. These participants were divided into two equal groups: a sample group, those who received a HEMS, and a control group, those who did not receive a HEMS. Participants from both groups were asked to complete two surveys and allow their electricity consumption data to be analyzed. The initial survey was to establish some baseline information about the participants. The second survey was designed to determine if changes had occurred in the household since the initial baseline survey. Through the analysis of the survey and households electricity consumption data, conclusions were drawn on how participants used the HEMS. The study had a 2.9% relative reduction in total electricity consumption and a 13.2% relative reduction in on-peak electricity consumption. However, additional analysis of the results revealed promising findings with regard to the HEMS ability to catalyze conservation and demand management among recent time-of-use pricing adopters.

A method has been developed, Physical Hybrid Model, to investigate the physical large scale electrical effects of a Battery Energy Storage System (BESS) on a distribution grid by scaling the response from a small size Research Development and Demonstration (RD&D) platform. In order to realize the model the control system of an existing RD&D platform was refurbished and stability of components ensured. The Physical Hybrid Model proceeds as follows: Data from a distribution grid are collected. A BESS cycle curve is produced based on analyzed measurements. Required BESS power and capacity in investigated grid is scaled down by factor k to that of the physical test installation of the RD&D platform. The scaled BESS cycle is sent as input to control of the battery cycling of the RD&D platform. The response from the RD&D platform is scaled – up, and used in simulation of the distribution grid to find the impact of a BESS. The model was successfully implemented on a regional distribution grid in southern Sweden.

Abstract This thesis proposes a PC based electric energy management system as well as load control strategies for demand side management in high voltage customer. Besides, this thesis proposes a sequential search method for the decision of optimal demand contract. By the proposed approach, We expect to decrease the basic demand charge and the total electrical cost. The load survey and load characteristics of selected high voltage customers are first fulfilled to derive the load composition and statistic data for large air conditioner. Furthermore,digital power meters are installed at each substation and they are connected in star configuration with telephone network to form automatic meter reading system. Power parameters such as V, I, P, Q, P.F. etc. are periodically collected via telephone network. By inspecting the trend of peak load as well as the load composition, the specification and structure of electric energy management system and their application functions are difined. The proposed PC based electric energy management system is integrated programmable logic controller (PLC) with power meters to form basic Supervisory Control And Data Acquisition ¡]SCADA¡^functions. Besides, advance functions such as demand monitoring/load shedding, periodical load control, clock load control, direct load control, alarm, and real time/historical trending are embedded to enhance the capability of proposed system. By the Visual Function Block in Diamond Control View, automatic meter reading system can be simulated and demonstrated. The academic power system in National Sun Yat-Sen University(NSYSU) are selected for testing to demonstrate the effectiveness of proposed system. Finally,the effect of peak load cutting will not only save energy consumption of the customer but also increase the power capacity of substations for Taiwan power system.

Nowadays, the increasing amount of greenhouse gases and diminishing of the existing petroleum minerals for future generations, has led the automotive companies to think of producing vehicles with less emissions and fuel consumption. For this purpose, Hybrid Electric Vehicles (HEVs) have emerged in the recent decades. HEVs with different configurations have been introduced by engineers.The simulation platform aim for a parallel HEV, where the intention is to reduce the emissions and fuel consumption. The simulation platform includes an Electric Motor (EM) in addition to an Internal Combustion Engine (ICE). A new transmission system is modeled which is compatible with parallel configuration for the HEV, where the inertial effects of the gearbox, clutch and driveline is formulated. The transmission system includes a gearbox which is equipped with synchronizers for smooth change of gears. The HEV is controlled by a rule based controller together with an optimization algorithm as power management strategy in order to have optimal fuel consumption. Using the rule based controller, the HEV is planned to be launched by EM in order to have a downsized clutch and ICE. The clutch modeling is the main focus of this study, where the slipping mechanism is considered in the simulation. In the driveline model, the flexibility effects of the propeller shaft and drive shaft is simulated, so that the model can capture the torsional vibrations of the driveline. The objective of modeling such a system is to reduce emissions and fuel consumption with the same performance of the conventional vehicle. To achieve this goal first a conventional vehicle is modeled and subsequently, a hybrid vehicle is modeled and finally the characteristics of the two simulated models are studied and compared with each other. Using the simulation platform, the state of charge (SOC) of the battery, oscillations of propeller shaft and drive shaft, clutch actuations and couplings, energy dissipated by the clutch, torques provided by EM and ICE, fuel consumptions, emissions and calculation time are calculated and investigated. The hybridization results in a reduction in fuel consumption and emissions, moreover, the energy dissipated by the clutch and clutch couplings are decreased.

Current computing infrastructures use virtualization to increase resource utilization by deploying multiple virtual machines on the same hardware. Virtualization is particularly attractive for data center, cloud computing, and hosting services; in these environments computer systems are typically configured to have fast processors, large physical memory and huge storage capable of supporting concurrent execution of virtual machines. Subsequently, this high demand for resources is directly translating into higher energy consumption and monetary costs. Increasingly managing energy consumption of virtual machines is becoming critical. However, virtual machines make the energy management more challenging because a layer of virtualization separates hardware from the guest operating system executing inside a virtual machine. This dissertation addresses the challenge of designing energy-efficient storage, memory and buffer cache for virtual machines by exploring innovative mechanisms as well as existing approaches. We analyze the architecture of an open-source virtual machine platform Xen and address energy management on each subsystem. For storage system, we study the I/O behavior of the virtual machine systems. We address the isolation between virtual machine monitor and virtual machines, and increase the burstiness of disk accesses to improve energy efficiency. In addition, we propose a transparent energy management on main memory for any types of guest operating systems running inside virtual machines. Furthermore, we design a dedicated mechanism for the buffer cache based on the fact that data-intensive applications heavily rely on a large buffer cache that occupies a majority of physical memory. We also propose a novel hybrid mechanism that is able to improve energy efficiency for any memory access. All the mechanisms achieve significant energy savings while lowering the impact on performance for virtual machines.

Gato del Sol III, was powered by a solar array of 480 Silicon mono-crystalline photovoltaic cells. Maximum Power Point trackers efficiently made use of these cells and tracked the optimal load. The cells were mounted on a fiber glass and foam core composite shell. The shell rides on a lightweight aluminum space frame chassis, which is powered by a 95% efficient brushless DC motor. Gato del Sol IV was the University of Kentucky Solar Car Team’s (UKSCT) entry into the American Solar Car Challenge (ASC) 2010 event. The car makes use of 310 high density lithium-polymer batteries to account for a 5 kWh pack, enough to travel over 75 miles at 40 mph without power generated by the array. An in-house battery protection system and charge balancing system ensure safe and efficient use of the batteries. Various electrical sub-systems on the car communicate among each other via Controller Area Network (CAN). This real time data is then transmitted to an external computer via RF communication for data collection.

Today, issues such as climate change and increased competition for scarce resources puts pressure on society and firms to transform. Change is not easily managed though, especially not when relating to production or consumption of energy carriers such as district heating or electric power. These systems do not only have strong dynamics internally, but dynamics between multiple technological systems must sometimes be considered to effectively manage response and strategies in relation to change. During the early 1980s, an optimisation model founded on an expert-based approach was developed based on the partial equilibrium model to enable the evaluation of different actions to reach a target. This model — often referred to as marginal abatement cost curve (MACC) or conservation supply curve (CSC) — is used by academia, industry and policymakers globally. The model is applied for causes such as energy conservation and waste management, but also within the climate change context for optimising CO2 reductions and governmental policy. In this context, the model is used by actors such as the Intergovernmental Panel on Climate Change (IPCC), International Energy Agency (IEA) and World Bank, and by the consultancy firm McKinsey &amp; Company, who use it extensively in different analysis. This model has many drawbacks in relation to managing interdependencies between different options, but more specifically the metric used for ranking options with a negative marginal cost has a design flaw leading to biased results. As a solution Pareto optimisation has been suggested, but is problematic given the dynamics within and between energy systems. The purpose of this compilation dissertation is to improve the ability for industry and policymakers to effectively manage change and reach set targets. In particular it develops our knowledge of how to account for option interdependency within and between technological systems. Furthermore, the ranking problem relating to expert-based least cost integrated planning is addressed. This dissertation also provides policy and managerial implications relating to the issues of energy conservation, CO2 abatement, and SOx and NOx reduction in relation to the district heating system in Stockholm. Implications are also provided for the interaction with other systems such as the Nordic electric power system. / Klimatfrågan och konkurrens om knappa resurser medför ett förändringstryck på nationer och företag. Att hantera förändringar har aldrig varit enkelt, vilket är tydligt bland företag inom energisektorn såsom el och fjärrvärmeproducenter. Energisystemen dessa företag är del av har stark intern dynamik, men även dynamik mellan olika energisystem är vanligt. Detta måste tas i beaktande när strategier och planer för att hantera förändring utformas. Under början av 1980-talet skapades en optimeringsmodell baserad på den nationalekonomiska jämviktsmodellen för att kunna utvärdera olika specifika möjligheter att nå ett mål, t.ex. energibesparingar. Denna modell, som idag ofta benämns MACC (Marginal Abatement Cost Curves) eller CSC (Concervation Supply Curves), används idag av akademin, industrin och myndigheter inom områden så som energibesparingar, minskade CO2-utsläpp, sophantering och design av ekonomiska policyinstrument. De icke-akademiska användarna inkluderar FNs klimatorgan IPCC, IEA och Världsbanken. Även konsultfirman McKinsey&amp;Company använder modellen regelbundet i olika studier. Tyvärr har modellen många begräsningar när det kommer till att hantera dynamiker mellan de specifika åtgärder som identifierats för att nå ett mål. Den allvarligast begränsningen utgörs dock av ett optimeringsfel som leder till felaktiga slutsatser om prioriteringen mellan de åtgärder som har en negativ marginalkostnad. Som en lösning på detta problem har pareto-optimering föreslagits, vilket denna avhandling dock visar är problematiskt på grund av de dynamiker som finns inom och mellan energisystem. Det övergripande syftet med denna avhandling är att förbättra möjligheten att hantera förändringar och nå uppsatta mål. Specifikt diskuteras hur beroenden mellan olika åtgärder för att nå det satta målet kan hanteras. Avhandlingen adresser även problemet att prioritera mellan åtgärder med negativ marginalkostnad. Utöver detta bidrar avhandlingen med praktiska implikationer för politiker, myndigheter och företag involverade i fjärrvärmeproduktion i Stockholm. Slutsatser dras kring energibesparingar och minskade utsläpp av CO2, SOx och NOx. Praktiska implikationer ges även för hur system som detta fjärrvärmesystem samverkar och interagerar med det nordiska elsystemet. / <p>QC 20150414</p> / Investments in energy efficiency and climate change abatement: revising marginal cost curves as an optimization model