Abstract

In literature, several mechanisms are proposed to prevent Plug-in Electric Vehicles (PEVs) from overloading the distribution grid. However, it is unclear how such technical mechanisms influence the market level control strategies of a PEV aggregator. Moreover, the presence of multiple aggregators in the same distribution grid further complicates the problem. Often, grid congestion management mechanisms are proposed to solve the potential interference between the technical and market objectives. Such methods come at the expense of additional complexity and costs, which is not beneficial for the large scale application of demand response. In our work, we investigate this problem by combining a simple low level voltage droop controller with an event driven control strategy for the coordination of charging PEVs. The approach is evaluated in different distribution grid settings, using two different market objectives for the aggregator. Document type: Part of book or chapter of book

Abstract

paradigm shift has to be realized in future energy systems with high shares of renewable energy sources. The electrical demand has to react to the fluctuating electricity generation of renewable energy sources. To this end, flexible electrical loads like electric heating devices coupled with thermal storage or electric vehicles are necessary in combination with optimization approaches. In this paper, we develop a novel privacy-preserving approach for decentralized optimization to exploit load flexibility. This approach, which is based on a set of schedules, is referred to as SEPACO-IDA. The results show that our developed algorithm outperforms the other approaches for scheduling based decentralized optimization found in the literature. Furthermore, this paper clearly illustrates the suboptimal results for uncoordinated decentralized optimization and thus the strong need for coordination approaches. Another contribution of this paper is the development and evaluation of two methods for distributing a central wind power profile to the local optimization problem of distributed agents (Equal Distribution and Score-Rank-Proportional Distribution). These wind profile assignment methods are combined with different decentralized optimization approaches. The results reveal the dependency of the best wind profile assignment method on the used decentralized optimization approach.

Abstract

The shift towards more renewable energy sources is imminent, this shift is accelerated by the technological advancement and the rise of environmental awareness. However, this shift causes major operational problems to the current grid that is optimised for unidirectional power flow. Besides the operational problems, there are problems related to the optimal tariff scheme. In this thesis a study on the effect of the adoption of photovoltaic solar panels and the electric vehicles on the households' electricity demand profile is presented. The change on the demand profile is going to affect the current tariffs, this effect is also explored in this thesis. In this thesis real life data on household electricity use and photovoltaic power production was used. For electric vehicle charging simulated data was used. Besides that, a demand response scheme for electric vehicle is proposed in order to estimate the savings potential of this demand response on the electricity bill. The results show that the change in the demand profile is not merely a change in the total energy consumption, but it is a change in the power peaks as well. The peaks change significantly in condominiums and rental apartments, in this households' type it increases by around 80%, while in detached and row houses little change is noticed on the peaks, yet they still increase by around 10%. The demand response shows around 1- 12% savings in the distribution bill depending on the household, however it showed more incentives for condominiums and rental apartments. The current distribution tariffs perform asymmetrically with the various households. However, one tariff ensures 11.7 MSEK financial revenue for the distribution system operator, this is higher than the other tariffs' revenue by more than 28.5%. The new prospective situation requires totally different tariffs that ensure a balance between firstly a reasonable revenue for the distribution system operator and secondly incentives for consumers to self produce electricity as well as to reduce their peaks. 

Abstract

The electrical grid is a huge and complex system which represents a critical infrastructure. Due to this fact, the electric power industry has traditionally adopted a conservative attitude regarding changes. As a result of that, the electrical grid has experienced very few breakthroughs for decades and currently is not prepared to face novel challenges, such as properly integrating DERs (Distributed Energy Resources) or proactively controlling the energy demand by means of the so-called DR (Demand Response) programs, which mainly derive from nowadays society concerns on global warming and climate change. Upgrading traditional electrical grid to the so-called Smart Grid represents one of the most complex engineering projects ever and will certainly drive the next wave of research and innovation in both the energy and the ICT (Information and Communications Technology) sectors. The road towards the Smart Grid will mean an unprecedented revolution especially at the power distribution and customer domains, since the unpredictable and uncontrollable nature of renewables will impose the coordination of generation and consumption points in almost real time. M2M (Machine-to-Machine) communications allow networked devices to communicate between them without further human intervention. What in the very beginning seemed to be a tailored solution for telemetry applications, has become a communications paradigm itself, addressing the myriad of applications existing and yet to be in the wide context of the Internet of Things. As a matter of fact, M2M communications represent one of the main pillars of the Smart Grid in that they will enable the bidirectional real-time exchange of information between the consumption and generation facilities to be monitored and controlled, and the information systems where the optimization processes run. There is a plethora of communications technologies and protocols available within the scope of M2M communications for the Smart Grid. Hence, research is needed in two directions. On the one side, it is required to evaluate how different communications architectures and technologies meet the specific requirements of the Smart Grid before undertaking the important investments needed to deploy this kind of infrastructures on a large scale. On the other side, it is crucial to develop common data models which serve as reference to future horizontal or wide scope protocols which expand across different domains or areas. This thesis aims to tackle these issues. The main goal of the thesis is to contribute to the area of M2M communications architectures tailored to the power distribution and customer domains of the Smart Grid. In order to achieve this overall objective, first we carry out a survey on the most relevant standardization activities developed in parallel to this thesis and on the most outstanding technological and research trends within the Smart Grid area, identifying gaps and challenges. Second, we propose a novel M2M communications architecture to support energy efficiency and optimum coordination of DER (Distributed Energy Resources) within the so-called energy-positive neighborhoods, which are neighborhoods which ensure a substantial part of their consumption by local generation based on renewables. The proposed architecture comprises three network segments, for the sake of flexibility and scalability, and combines different communications technologies to meet the specific communications requirements of each of them. Next, we model formally the domain of knowledge of energy efficiency platforms for energy-positive neighborhoods by means of an ontology developed in OWL (Ontology Web Language), with the aim that it becomes a reference data model for the application of M2M communications to this context. Thus, this ontology has been made public through the EC (European Commission) eeBuildings Data Model community, so that other researches can re-use it and extend it. We also propose a methodology that can be applied, in general, to characterize any communications overlay deployed on top of an infrastructure devoted to any purpose. Following this methodology, we model the traffic of the proposed M2M communications architecture in realistic large-scale scenarios. The main goal of this model is to ensure that potential works based on it actually mean and bring value to the interested parties. Although the model is tailored to the Portuguese power distribution grid, since it is based on actual data provided by EDP (Energias de Portugal), it can be easily adapted to other scenarios by suitably tuning the appropriate parameters. Taking this model as reference, we finally evaluate the core of the proposed M2M communications architecture twofold. On the one side, we analyze the impact of using IPSec (Internet Protocol Security) or TLS/SSL (Transport Layer Security/Secure Socket Layer) as VPN (Virtual Private Network) technologies on the operational costs of a potential energy efficiency platform which relies on the proposed M2M communications architecture. To the author’s best knowledge, no similar studies are available in the state of the art. The main conclusion of this analysis is that using TLS/SSL along with data aggregation is the best option to minimize operational costs at neighborhood level. On the other side, we evaluate by means of simulations the performance of IEEE 802.11b, using as metric the goodput (i.e., throughput at the application layer), and GPRS (General Packet Radio Service), using as metric the transmission time. The first conclusion of these simulations is along the line that IEEE 802.11b meets the requirements in terms of goodput of the NAN (Neighborhood Area Networks), which is of special interest to the Smart Grid community taking into account the low cost and wide adoption of this technology. The second conclusion of such simulations is that GPRS meets the requirements in terms of bandwidth of the backhaul network, thus confirming that it represents a very attractive technology considering that it is the most mature and widely deployed cellular technology in Europe. Presidente: Jürgen Jähnert; Vocal: David Fernández Cambronero; Secretario: Francisco Valera Pintor

Abstract

The large penetration of intermittent resources, such as solar and wind generation, involves the use of storage systems in order to improve power system operation. Electric Vehicles (EVs) with gridable capability (V2G) can operate as a means for storing energy. This paper proposes an algorithm to be included in a SCADA (Supervisory Control and Data Acquisition) system, which performs an intelligent management of three types of consumers: domestic, commercial and industrial, that includes the joint management of loads and the charge/discharge of EVs batteries. The proposed methodology has been implemented in a SCADA system developed by the authors of this paper – the SCADA House Intelligent Management (SHIM). Any event in the system, such as a Demand Response (DR) event, triggers the use of an optimization algorithm that performs the optimal energy resources scheduling (including loads and EVs), taking into account the priorities of each load defined by the installation users. A case study considering a specific consumer with several loads and EVs is presented in this paper.

Abstract

Energy markets which are based on fixed day-ahead energy prices could face bottlenecks due to the simultaneous activation of flexible loads in the system and hence causing power imbalances and possibly also causing congestion in the distribution network. In order to mitigate these problems a suitable scheme is required to reschedule the flexible loads based on incentives for the end users for changing their demand from their day-ahead optimized demand pattern. This paper proposes a three-layer architecture of intelligent agents for scheduling of flexible loads which implies a demand bidding strategy. A real case study is considered from Nordpool spot prices for four different seasons of the year to show case the rationality of the study. Two flexible loads, Electric Vehicles (EVs) and Storage Space Heating (SSH) are considered in this scenario, while other loads are assumed non-flexible. Analysis of energy cost based on flexibility indices of loads is studied, which shows that flexibility of loads not only supports the power network capacity in congestion situation but it also benefits the consumers with cost effective use of energy. From aggregator’s perspective, the system resources are utilized optimally hence reducing the need for running extra generators and the spikes at the cheap price hours are mitigated. From the results, it is noted that the proposed bidding scheme has benefited the households up to 60% of the energy cost saving by participating in the demand response. The number of times each household wins a bid depends on the flexibility of loads and in this particular simulation scenario most of the households are able to win the bids 2 times in a day.

Abstract

The electric vehicle is presented as an environmental friendly alternative to common vehicles and as the future of transportation. However, car manufacturers consider that electric vehicle batteries are not useful for traction purposes when they reach a state of health of 80%. Thus, as a matter of fact, these batteries are sold knowing that they have a premature obsolescence, as they still have 80% of useful capacity that could be used elsewhere. This study analyzes economically and in terms of ageing performance the possibility to provide a second life to these batteries in buildings. The study presents several scenarios depending on the battery use, considering independent buildings or getting closer to the concept of smart grid with demand response services where buildings could participate in secondary electricity markets by means of an energy aggregator. Moreover, the study analyzes the existing European markets that allow aggregated demand response services. Results show that, effectively, the reuse of batteries for residential purposes might not be the best economical option even though their lifespan is enlarged four years more. Nonetheless, if they are able to participate in secondary electricity markets in addition to their normal use in buildings, the business becomes juicy enough having a relatively low impact on ageing. The promotion of electric vehicle battery reuse is necessary, as there is a large amount of batteries with a huge useful potential in stationary applications coming in the nearby future from the electric vehicles that have been sold during the last five years. If possible, car manufacturers should consider eco-design in order to facilitate this battery repurposing and lifespan enlargement. Peer Reviewed

Abstract

Electric Vehicles (EVs) are active loads as they increase the demand for electricity and introduce several challenges to electrical distribution feeders during charging. Demand Response (DR) or performing load control in commercial buildings along with the deployment of solar photovoltaic (PV) and ice storage systems at the building level can improve the efficiency of electricity grids and mitigate expensive peak demand/energy charges for buildings. This research aims to provide such a solution to make EV penetration transparent to the grid. Firstly, this research contributes to the development of an integrated control of major loads, i.e., Heating Ventilation and Air Conditioning (HVAC), lighting and plug loads while maintaining occupant environmental preferences in small- and medium-sized commercial buildings which are an untapped DR resource. Secondly, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-minute resolution data set at the individual plug load level. The research evaluates total building power consumption performance taking into account interactions among lighting, plug load, HVAC and control systems in a realistic manner. Third, this research presents a model to study integrated control of PV and ice storage on improving building operation in demand responsive buildings. The research presents the impact of deploying various combinations of PV and ice storage to generate additional benefits, including clean energy generation from PV and valley filling from ice storage, in commercial buildings. Fourth, this research presents a coordinated load control strategy, among participating commercial buildings in a distribution feeder to optimally control buildings' major loads without sacrificing occupant comfort and ice storage discharge, along with strategically deployed PV to absorb EV penetration. Demand responsive commercial building load profiles and field recorded EV charging profiles have been added to a real world distribution circuit to analyze the effects of EV penetration, together with real-world PV output profiles. Instead of focusing on individual building's economic benefits, the developed approach considers both technical and economic benefits of the whole distribution feeder, including maintaining distribution-level load factor within acceptable ranges and reducing feeder losses. Ph. D.

Abstract

Today’s electric grid must be transformed to meet modern consumption behaviors and safely integrate renewable energy sources. This has led to major efforts to develop grid-scale energy management solutions and ensure safety and reliability of our modern power network. In particular, large penetrations of Plug-in Electric Vehicles (PEVs) are expected increase energy needs and peak consumption, which would bring new challenges for utilities and grid operators.In this work, we develop optimization methods to coordinate the charging of large fleets of PEVs in distribution grids. We show that different methods should be applied, based on the infrastructure requirements and the objective of the controller.The first Chapter Optimal Charging of Fleets of Electric Vehicles with Discrete Charging rates: PDE Modeling and Control Techniques presents a continuum modeling framework to coordinate PEV charging with discrete charging rates. We consider PEVs as loads, which diffuse along the State Of Energy (SOE) axis, and can be in three different categories: charging, discharging or idle. We use a discretized form of Partial Differential Equations (PDEs) to model the dynamics of the system and control the transitions between each category. The second Chapter Dual Splitting Framework for Optimal Charging of Fleets of Electric Vehicles with Continuous Charging rates proposes a tailored distributed optimization method to coordinate PEV charging for load shaping. Three iteration methods are presented and their convergence characteristics are detailed. The third Chapter Electric Vehicle Charging in the Smart Grid: Plug-and-Play Model Predictive Control techniques studies a voltage-regulation scenario for PEV charging. Power flow and distribution grid constraints are modeled, and PEV charging is controlled with Plug-and-Play Model Predictive Control. Finally, the final chapter Behavioral study of Demand Response programs studies the impact of non rational choices on energy consumption and on the success of Demand Response programs.