Advanced Metering and Demand Response communication performance in Zigbee based HANs

Using IEEE 802.15.4 and Zigbee for home area networks (HANs) in the Smart Grid is becoming an increasingly prominent topic in the research area. As the standard designed for low data rate and low cost wireless personal area networks, IEEE 802.15.4 is widely employed in the construction of home sensor networks to assist with real-time environment information. For the purposes of Smart Grid the Zigbee Alliance has defined new Smart Energy Profile Protocol that leverages the existing TCP and HTTP protocols. In this paper, we provide an overview of the Smart Grid's Advanced Metering Infrastructure (AMI) and Demand Response (DR) functionalities, and the communication requirement they pose for the new SEP protocol. The discussion is followed by an evaluation of the theoretical performance bounds of the new architecture based on a analytical model. We conclude, by extending the model to account for WiFi interference which is expected to be present in home and office environments. © 2013 IEEE

Impact of realistic communications for fast-acting demand side management

The rising penetration of intermittent energy resources is increasing the need for more diverse electrical energy resources that are able to support ancillary services. Demand side management (DSM) has a significant potential to fulfil this role but several challenges are still impeding the wide-scale integration of DSM. One of the major challenges is ensuring the performance of the networks that enable communications between control centres and the end DSM resources. This paper presents an analysis of all communications networks that typically participate in the activation of DSM, and provides an estimate for the overall latency that these networks incur. The most significant sources of delay from each of the components of the communications network are identified which allows the most critical aspects to be determined. This analysis therefore offers a detailed evaluation of the performance of DSM resources in the scope of providing real-time ancillary services. It is shown that, using available communications technologies, DSM can be used to provide primary frequency support services. In some cases, Neighbourhood Area Networks (NANs) may add significant delay, requiring careful choice of the technologies deployed

Benefits of using virtual energy storage system for power system frequency response

This paper forms a Virtual Energy Storage System (VESS) and validates that VESS is an innovative and cost-effective way to provide the function of conventional Energy Storage Systems (ESSs) through the utilization of the present network assets represented by the flexible demand. The VESS is a solution to convert to a low carbon power system and in this paper, is modelled to store and release energy in response to regulation signals by coordinating the Demand Response (DR) from domestic refrigerators in a city and the response from conventional Flywheel Energy Storage Systems (FESSs). The coordination aims to mitigate the impact of uncertainties of DR and to reduce the capacity of the costly FESS. The VESS is integrated with the power system to provide the frequency response service, which contributes to the reduction of carbon emissions through the replacement of spinning reserve capacity of fossil-fuel generators. Case studies were carried out to validate and quantify the capability of VESS to vary the stored energy in response to grid frequency. Economic benefits of using VESS for frequency response services were estimated

Diagnóstico de la demanda del consumo de energía eléctrica en un Smart Home, enfocado en el sector residencial de Quito, durante el año 2015, barrio La Kennedy. Caracterización y optimización del consumo de energía eléctrica.

The main goal of this project is to present a possible solution to a problem that is been created by the advance of technology and the implementation of Smart cities “Smart Grid” through studying the characterization and modeling the daily electricity demand curve. This curve considers the growth of technology and therefore the increase of consumption of electricity of residential users, generating a significant impact on the demand curve and the users’ economy. For this reason, the suggested optimization will allow a balance between comfort and energy consumption of users. The research is divided into four chapters: the first one presents the state of art for demand modeling and optimization, the second one develops the research methodology and determines the sample where the surveys will take place. The third chapter deals with tabulation of information obtained in surveys conducted in Kennedy neighborhood in Quito. Also, it includes an analysis of the measurement data by user type energy analyzer. Chapter IV, that is the last one, develops the proposal by modeling the demand by Markov Chains and Monte Carlo (MCMC). This modeling established different scenarios, which characterize the energy consumption and the optimization that was performed by means of the Pareto multi objective method. The problem was solved through experimenting a simulation and modeling a demand to optimize energy, generating a 20% of savings in electricity consumption, generating a benefit to the environment and reducing CO2 emissions; without changing the habits of users.El avance de la tecnología y la implementación de las ciudades Inteligentes Smart Grid presenta un problema por lo que el presente: resolver, estudiar, caracterizar y modelar la curva de demanda eléctrica diaria, la cual considera el crecimiento de la tecnología y por ende el incremento del consumo de energía eléctrica en usuarios residenciales, generando un impacto importante en la curva de la demanda y en la economía de los hogares, razón por lo cual la optimización permitirá tener un equilibrio entre confort y el consumo de energía eléctrica de los usuarios. La investigación se divide en cuatro capítulos el primero el estado de arte para la modelación de la demanda y la optimización, analizando las diferentes investigación, el segundo capítulo se desarrolla la metodología de la investigación y se determina la muestra con la cual se realizarán las encuestas en el barrio La Kennedy de la ciudad de Quito, mediante la aplicación de encuestas y la medición de la energía por medio de analizadores de red, en el tercer capítulo se realizó la tabulación de la información obtenida en las encuestas, además se analizó los datos medidos en un usuario tipo mediante un analizador de energía y finalmente en el capítulo IV se desarrolla la propuesta mediante la modelación de la demanda por Las Cadenas se Markov y Montecarlo (MCMC), esta modelación estableció diferentes escenarios, los cuales caracteriza el consumo de energía, la misma que por medio del método de multiobjetivo de Pareto se realizó la optimización. El problema fue resuelto por la investigación mediante la simulación y modelamiento de la demanda, para luego optimizar la energía, generando un ahorro en el consumo de la electricidad en un 20%, generando un beneficio al medio ambiente y reduciendo las emisiones de CO2.; sin cambiar las costumbres de los usuarios

SECURE REAL-TIME SMART GRID COMMUNICATIONS: A MICROGRID PERSPECTIVE

Microgrids are a key component in the evolution of the power grid. Microgrids are required to operate in both grid connected and standalone island mode using local sources of power. A major challenge in implementing microgrids is the communications and control to support transition from grid connected mode and operation in island mode. In this dissertation we propose a distributed control architecture to govern the operation of a microgrid. The func- tional communication requirements of primary, secondary and tertiary microgrid controls are considered. Communication technology media and protocols are laid out and a worst-case availability and latency analysis is provided. Cyber Security challenges to microgrids are ex- amined and we propose a secure communication architecture to support microgrid operation and control. A security model, including network, data, and attack models, is defined and a security protocol to address the real-time communication needs of microgrids is proposed. We propose a novel security protocol that is custom tailored to meet those challenges. The chosen solution is discussed in the context of other security options available in the liter- ature. We build and develop a microgrid co-simulation model of both the power system and communication networks, that is used to simulate the two fundamental microgrid power transition functions - transition from island to grid connected mode, and grid connected to island mode. The proposed distributed control and security architectures are analyzed in terms of performance. We further characterize the response of the power and communication subsystems in emergency situations: forced islanding and forced grid modes. Based on our findings, we generalize the results to the smart grid

Exploiting Mobile Energy Storages for Overload Mitigation in Smart Grid

The advancement of battery and electronic technologies pushes forward transportation electrification, accelerating the commercialization and prevalence of plug-in electric vehicles (PEVs). The development of PEVs is closely related to the smart grid as PEVs are considered as high power rating electric appliances that require frequent charging. As PEVs become regular transportation options, charging stations (CSs) are also extensively deployed in the smart grid to meet the PEV charging demand. During peak traffic hours, the increasing PEV charging demand could exceed the loading capacities of CS-connected transformers, causing heavy charging overload in-station. Without proper overload mitigation, the energy imbalance issues will result in severe feeder degradation and power quality issue. Therefore, solutions for CS overload mitigation are in urgent demand. Considering the rechargeable nature of PEV batteries, PEVs can serve as potential mobile energy storages (MESs) to carry energy from power nodes with excess energy to overloaded CSs to compensate the overloads. Compared to infrastructure upgrade and installing stationary energy storages at CSs, the utilization of PEVs not only minimizes the additional upgrade/installation expenditure, but also maximizes the energy utilization in the smart grid with high flexibility. However, the PEV utilization for overload mitigation is confronted with a variety of challenges due to vehicular mobility and the fear of battery degradation. Because of vehicular mobility, the CS operation dynamics become stochastic processes, increasing the difficulty of the CS demand estimation. Without accurate demand estimation, the overload condition cannot be timely predicted and controlled. Moreover, the stochastic on-road traffic could impair the time-efficiency of the PEV overload mitigation service. Further, as the overload mitigation service demands frequent charging and discharging, the fear of battery degradation could impede PEV owners from providing the service, making the overload mitigation tasks harder to fulfill. In this thesis, we address the above challenges to effectively utilize PEVs for overload mitigation in the smart grid. In specific, different approaches are designed according to the PEV properties at different commercialization stages. First, at the early PEV commercialization stage, power utility company purchases large battery capacity PEVs as utility-owned MESs (UMESs) whose only responsibility is fulfilling the energy compensation task. The fleet of UMESs is rather small due to the company's limited budget, and therefore UMESs priorly serve the CSs with large energy imbalance (e.g., 500-1000kWh). Thus, the stochastic CS charging demand needs to be accurately estimated and then UMESs can be scheduled to these CSs for overload mitigation. To achieve this objective, we develop a two-dimensional Markov Chain model to characterize the stochastic process in-station so that the CS charging demand can be precisely estimated. Based on the estimated CS demand status, a two-tier energy compensation framework is designed to schedule UMESs to the heavily overloaded CSs in a timely and cost-efficient manner. Second, at the medium stage of PEV commercialization, vehicle-fleet based companies are motivated by legislation to purchase a large fleet of PEVs which can be served as potential MESs, referred to as legislation-motivated MESs (LMESs). To deliver energy to overloaded CSs using LMESs would introduce a large amount of additional traffics to the transportation network. When injecting these LMES traffics into an already busy transportation network, unexpected traffic delay could occur, delaying the overload mitigation service. To avoid the potential traffic delay incurred by LMES service, we develop an energy-capacitated transportation network model to measure the road capacity of accommodating additional LMES traffics. Based on the developed model, a loading-optimized navigation scheme is proposed to calculate the optimal navigation routes for LMES overload mitigation. To stimulate LMESs following the optimal navigation, we propose a dynamic pricing scheme that adjusts the service price to align the LMES service routes with the optimal routes to achieve a time-efficient service result. Third, when PEVs are prevalent in the automobile market and become regular transportation options for every household, on-road private-owned PEVs can be efficiently used as energy porters to deliver energy to overloaded CSs, named as private MESs (PMESs). As the primary objective of PMESs is to reach their planned destinations, the monetary incentive is demanded to stimulate them actively participating in the overload mitigation tasks. Therefore, a hierarchical decision-making process between the utility operator (UO) and PMESs is in demand. Moreover, considering PMESs have different service preferences (e.g., the fear of battery degradation, the unwillingness of long service time, etc.), individual PMES decision making process on the task should be carefully modelled. Thus, we propose to characterize the price-service interaction between the operator and PMESs as a Stackelberg game. The operator acts as the leader to post service price to PMESs while PMESs act as followers, responding to the posted price to maximize their utility functions. In summary, the analysis and schemes proposed in this thesis can be adopted by the local power utility company to utilize PEVs for overload mitigation at overloaded power nodes. The proposed schemes are applicable during different PEV commercialization stage and present PEVs as a flexible solution to the smart grid overload issue