Browsing by Subject "Smart grid"

Now showing 1 - 13 of 13

Analysis of transmission system events and behavior using customer-level voltage synchrophasor data
(2013-05) Allen, Alicia Jen; Santoso, Surya
The research topics presented in this dissertation focus on validation of customer-level voltage synchrophasor data for transmission system analysis, detection and categorization of power system events as measured by phasor measurement units (PMUs), and identification of the influence of power system conditions (wind power, daily and seasonal load variation) on low-frequency oscillations. Synchrophasor data can provide information across entire power systems but obtaining the data, handling the large dataset and developing tools to extract useful information from it is a challenge. To overcome the challenge of obtaining data, an independent synchrophasor network was created by taking synchrophasor measurements at customer-level voltage. The first objective is to determine if synchrophasor data taken at customer-level voltage is an accurate representation of power system behavior. The validation process was started by installing a transmission level (69 kV) PMU. The customer-level voltage measurements were validated by comparison of long term trends and low-frequency oscillations estimates. The techniques best suited for synchrophasor data analysis were identified after a detailed study and comparison. The same techniques were also applied to detect power system events resulting in the creation of novel categories for numerous events based on shared characteristics. The numerical characteristics for each category and the ranges of each numerical characteristic for each event category are identified. The final objective is to identify trends in power system behavior related to wind power and daily and seasonal variations by utilizing signal processing and statistical techniques.
Autonomous trading in modern electricity markets
(2015-12) Urieli, Daniel; Stone, Peter, 1971-; Mooney, Raymond; Ravikumar, Pradeep; Baldick, Ross; Kolter, Zico
The smart grid is an electricity grid augmented with digital technologies that automate the management of electricity delivery. The smart grid is envisioned to be a main enabler of sustainable, clean, efficient, reliable, and secure energy supply. One of the milestones in the smart grid vision will be programs for customers to participate in electricity markets through demand-side management and distributed generation; electricity markets will (directly or indirectly) incentivize customers to adapt their demand to supply conditions, which in turn will help to utilize intermittent energy resources such as from solar and wind, and to reduce peak-demand. Since wholesale electricity markets are not designed for individual participation, retail brokers could represent customer populations in the wholesale market, and make profit while contributing to the electricity grid’s stability and reducing customer costs. A retail broker will need to operate continually and make real-time decisions in a complex, dynamic environment. Therefore, it will benefit from employing an autonomous broker agent. With this motivation in mind, this dissertation makes five main contributions to the areas of artificial intelligence, smart grids, and electricity markets. First, this dissertation formalizes the problem of autonomous trading by a retail broker in modern electricity markets. Since the trading problem is intractable to solve exactly, this formalization provides a guideline for approximate solutions. Second, this dissertation introduces a general algorithm for autonomous trading in modern electricity markets, named LATTE (Lookahead-policy for Autonomous Time-constrained Trading of Electricity). LATTE is a general framework that can be instantiated in different ways that tailor it to specific setups. Third, this dissertation contributes fully implemented and operational autonomous broker agents, each using a different instantiation of LATTE. These agents were successful in international competitions and controlled experiments and can serve as benchmarks for future research in this domain. Detailed descriptions of the agents’ behaviors as well as their source code are included in this dissertation. Fourth, this dissertation contributes extensive empirical analysis which validates the effectiveness of LATTE in different competition levels under a variety of environmental conditions, shedding light on the main reasons for its success by examining the importance of its constituent components. Fifth, this dissertation examines the impact of Time-Of-Use (TOU) tariffs in competitive electricity markets through empirical analysis. Time-Of-Use tariffs are proposed for demand-side management both in the literature and in the real-world. The success of the different instantiations of LATTE demonstrates its generality in the context of electricity markets. Ultimately, this dissertation demonstrates that an autonomous broker can act effectively in modern electricity markets by executing an efficient lookahead policy that optimizes its predicted utility, and by doing so the broker can benefit itself, its customers, and the economy.
Demand side load control in residential buildings with HVAC controller for demand response
(2015-05) Yoon, Ji Hoon; Baldick, Ross; Novoselac, Atila; Arapostathis, Aristotle; Liedl, Petra G; Kwasinski, Alexis
Demand Response (DR) is a key factor to increase the efficiency of the power grid and has the potential to facilitate supply-demand balance. Demand side load control can contribute to reduce electricity consumption through DR programs. Especially, Heating, Ventilating and Air Conditioning (HVAC) load is one of the major contributors to peak loads. In the United States, HVAC systems are the largest consumers of electrical energy and a major contributor to peak demand. In this research, the Dynamic Demand Response Controller (DDRC) is proposed to reduce peak load as well as saves electricity cost while maintaining reasonable thermal comfort by controlling HVAC system. To reduce both peak load and energy cost, DDRC controls the set-point temperature in a thermostat depending on real-time price of electricity. Residential buildings are modeled with various internal loads using building energy modeling tools. The weather data in different climate zones are used to demonstrate that DDRC decreases peak loads and brings economic benefit in various locations. In addition, two different types of electricity wholesale markets are used to generate DR signals. To assess the performance of DDRC, the control algorithms are improved to consider the characteristics of building envelopes and HVAC equipment. Also, DDRC is designed to be deployed in various areas with different electricity wholesale markets. The indoor thermal comfort on temperature and humidity are considered based on ASHRAE standard 55. Finally, DDRC is developed to a hardware using embedded system. The hardware of DDRC is based on Advanced RISC Microcontroller (ARM) processor and senses both indoor and outdoor environment with Internet connection capability for DR. In addition, user friendly Graphic User Interface (GUI) is generated to control DDRC.
Disaggregation of residential electric loads using smart metered data
(2011-05) Holcomb, Chris L.; Zarnikau, Jay; Baldick, Ross
The ability of typical utility meters and advanced meters including sub-circuit metering to disaggregate residential electric loads and determine what appliances a homeowner is using at a given time in investigated. The basics of residential electricity systems, instrumentation options, and characteristics of selected residential loads are presented. This information informs a discussion on present and future disaggregation algorithms. The conclusions highlight the importance of reactive power and current harmonics in determining power consumed and identifying modern electrical devices, and raise concerns related to the ability of typical 15 minute interval utility smart meters to disaggregate loads.
Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment
(2014-05) Cole, Wesley Joseph; Edgar, Thomas F.
This work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees of freedom by incorporating some form of energy storage (in this work, thermal energy storage), and the increasing penetration of smart grid technologies provides a wealth of data for both modeling and management. Data used for the system models here come primarily from the Pecan Street Smart Grid Demonstration Project in Austin, Texas, USA. Other data are from the Austin Energy Mueller Energy Center and the University of Texas Hal C. Weaver combined heat and power plant. Systems considered in this work include thermal energy storage, chiller plants, combined heat and power plants, turbine inlet cooling, residential air conditioning, and solar photovoltaics. These systems are modeled and controlled in integrated environments in order to provide system benefits. In a district cooling system with thermal energy storage, combined heat and power, and turbine inlet cooling, model-based optimization strategies are able to reduce peak demand and decrease cooling electricity costs by 79%. Smart grid data are employed to consider a system of 900 residential homes in Austin. In order to make the system model tractable for a model predictive controller, a reduced-order home modeling strategy is developed that maps thermostat set points to air conditioner electricity consumption. When the model predictive controller is developed for the system, the system is able to reduce total peak demand by 9%. Further work with the model of 900 residential homes presents a modified dual formulation for determining the optimal prices that produce a desired result in the residential homes. By using the modified dual formulation, it is found that the optimal pricing strategy for peak demand reduction is a critical peak pricing rate structure, and that those prices can be used in place of centralized control strategies to achieve peak reduction goals.
Dynamic optimization of energy systems with thermal energy storage
(2013-08) Powell, Kody Merlin; Edgar, Thomas F.
Thermal energy storage (TES), the storage of heat or cooling, is a cost-effective energy storage technology that can greatly enhance the performance of the energy systems with which it interacts. TES acts as a buffer between transient supply and demand of energy. In solar thermal systems, TES enables the power output of the plant to be effectively regulated, despite fluctuating solar irradiance. In district energy systems, TES can be used to shift loads, allowing the system to avoid or take advantage of peak energy prices. The benefit of TES, however, can be significantly enhanced by dynamically optimizing the complete energy system. The ability of TES to shift loads gives the system newfound degrees of freedom which can be exploited to yield optimal performance. In the hybrid solar thermal/fossil fuel system explored in this work, the use of TES enables the system to extract nearly 50% more solar energy when the system is optimized. This requires relaxing some constraints, such as fixed temperature and power control, and dynamically optimizing the over a one-day time horizon. In a district cooling system, TES can help equipment to run more efficiently, by shifting cooling loads, not only between chillers, but temporally, allowing the system to take advantage of the most efficient times for running this equipment. This work also highlights the use of TES in a district energy system, where heat, cooling and electrical power are generated from central locations. Shifting the cooling load frees up electrical generation capacity, which is used to sell power to the grid at peak prices. The combination of optimization, TES, and participation in the electricity market yields a 16% cost savings. The problems encountered in this work require modeling a diverse range of systems including the TES, the solar power plant, boilers, gas and steam turbines, heat recovery equipment, chillers, and pumps. These problems also require novel solution methods that are efficient and effective at obtaining workable solutions. A simultaneous solution method is used for optimizing the solar power plant, while a static/dynamic decoupling method is used for the district energy system.
Economic forecasting and optimization in a smart grid built environment
(2013-08) Sriprasad, Akshay; Edgar, Thomas F.
This Master’s Report outlines graduate research work completed by Akshay Sriprasad, who is supervised by Professor Tom Edgar, in the area of modeling and systems optimization for the smart grid. The scope this report includes the development and validation of strategies to elicit demand response, defined as reduction of peak demand, at the residential level, in conjunction with collaborative research efforts from the Pecan Street Research Institute, a smart grid research consortium based in Austin, TX. The first project outlined is an artificial neural network-‐based demand forecasting model, initially developed for UT’s campus cooling system and adapted for residential homes. Utilizing this forecasting model, a number of demand response-‐focused optimization studies are carried out, including optimization of community energy storage for peak shifting, and electric vehicle charging optimization to harness inexpensive night-‐time Texas wind energy. Community energy storage and electric vehicles are chosen as ideal dynamic charging media due to increased proliferation and focus of Pecan Street Research Institute on critical emerging technologies. As these two technologies involve significant capital investment, an alternative mobile application-‐based demand response strategy is outlined to complete a comprehensive portfolio of demand response strategies to suit a variety of budgets and capabilities.
Edge-of-grid voltage control in distribution networks
(2018-08) Padullaparti, Harsha Vardhana; Santoso, Surya; Baldick, Ross; Hallock, Gary; Korgel, Brian; Nikolova, Evdokia
As the electric power supply systems are undergoing major changes with the integration of renewables, the issues related to voltage regulation and system protection are arising. In this scenario, advanced voltage regulation technologies that provide voltage control at the grid-edge, that is at the low-voltage secondary side of the distribution circuit, have emerged as a potential solution to address the shortcomings of traditional voltage control practices in distribution systems. In this work, these technologies are modeled and algorithms are developed to strategically deploy them, tune their control parameters, and evaluate their voltage regulation performance. A two-stage optimization framework is proposed for optimal placement and real-time control of the low-voltage static var compensators to minimize the energy losses while maintaining the voltage regulation. Integration of high levels of distributed generation such as photovoltaic (PV) systems impacts the voltage regulation by causing steady-state voltage variations and transient voltage fluctuations. This work further develops a procedure to tune the control parameters of PV smart inverters to mitigate these voltage issues. Furthermore, the PV penetration levels in a distribution network can be increased without creating voltage problems by dynamic controlled reactive power absorption at several strategic buses. This concept is modeled and demonstrated in this work. Furthermore, the high levels of PV generation can interfere with the overcurrent protection schemes prevalent in distribution networks. An analytical approach is proposed in this work to estimate the distribution feeder PV accommodation limits with respect to overcurrent protection issues as the impact criteria, without needing to simulate numerous PV screening scenarios to assess the impact
Harnessing demand flexibility to minimize cost, facilitate renewable integration, and provide ancillary services
(2014-08) Kefayati, Mahdi; Baldick, Ross
Renewable energy is key to a sustainable future. However, the intermittency of most renewable sources and lack of sufficient storage in the current power grid means that reliable integration of significantly more renewables will be a challenging task. Moreover, increased integration of renewables not only increases uncertainty, but also reduces the fraction of traditional controllable generation capacity that is available to cope with supply-demand imbalances and uncertainties. Less traditional generation also means less rotating mass that provides very short term, yet very important, kinetic energy storage to the system and enables mitigation of the frequency drop subsequent to major contingencies but before controllable generation can increase production. Demand, on the other side, has been largely regarded as non-controllable and inelastic in the current setting. However, there is strong evidence that a considerable portion of the current and future demand, such as electric vehicle load, is flexible. That is, the instantaneous power delivered to it needs not to be bound to a specific trajectory. In this thesis, we focus on harnessing demand flexibility as a key to enabling more renewable integration and cost reduction. We start with a data driven analysis of the potential of flexible demands, particularly plug-in electric vehicle (PEV) load. We first show that, if left unmanaged, these loads can jeopardize grid reliability by exacerbating the peaks in the load profile and increasing the negative correlation of demand with wind energy production. Then, we propose a simple local policy with very limited information and minimal coordination that besides avoiding undesired effects, has the positive side-effect of substantially increasing the correlation of flexible demand with wind energy production. Such local policies could be readily implemented as modifications to existing "grid friendly" charging modes of plug-in electric vehicles. We then propose improved localized charging policies that counter balance intermittency by autonomously responding to frequency deviations from the nominal frequency and show that PEV load can offer a substantial amount of such ancillary services. Next, we consider the case where real-time prices are employed to provide incentives for demand response. We consider a flexible load under such a pricing scheme and obtain the optimal policy for responding to stochastic price signals to minimize the expected cost of energy. We show that this optimal policy follows a multi-threshold form and propose a recursive method to obtain these thresholds. We then extend our results to obtain optimal policies for simultaneous energy consumption and ancillary service provision by flexible loads as well as optimal policies for operation of storage assets under similar real-time stochastic prices. We prove that the optimal policy in all these cases admits a computationally efficient form. Moreover, we show that while optimal response to prices reduces energy costs, it will result in increased volatility in the aggregate demand which is undesirable. We then discuss how aggregation of flexible loads can take us a step further by transforming the loads to controllable assets that help maintain grid reliability by counterbalancing the intermittency due to renewables. We explore the value of load flexibility in the context of a restructured electricity market. To this end, we introduce a model that economically incentivizes the load to reveal its flexibility and provides cost-comfort trade-offs to the consumers. We establish the performance of our proposed model through evaluation of the price reductions that can be provided to the users compared to uncontrolled and uncoordinated consumption. We show that a key advantage of aggregation and coordination is provision of "regulation" to the system by load, which can account for a considerable price reduction. The proposed scheme is also capable of preventing distribution network overloads. Finally, we extend our flexible load coordination problem to a multi-settlement market setup and propose a stochastic programming approach in obtaining day-ahead market energy purchases and ancillary service sales. Our work demonstrates the potential of flexible loads in harnessing renewables by affecting the load patterns and providing mechanisms to mitigate the inherent intermittency of renewables in an economically efficient manner.
Modeling and simulation of distribution system components in anticipation of a smarter electric power grid
(2011-05) Toliyat, Amir; Kwasinski, Alexis; Grady, Wiliam
Successful development of the electric power grid of the future, hereinafter referred to as a smart grid, implicitly demands the capability to model the behavior, performance, and cost of distribution-level smart grid components. The modeling and simulation of such individual components, together with their overall interaction, will provide a foundation for the design and configuration of a smart grid. It is the primary intent of this thesis, to provide a basic insight into the energy transfer of various distribution-level components by modeling and simulating their dynamic behavior. The principal operations of a smart grid must be considered, including variable renewable generation, energy storage, power electronic interfaces, variable load, and plug-in electric vehicles. The methodology involves deriving the mathematical equations of components, and, using the MATLAB/Simulink environment, creating modules for each component. Ultimately, these individual modules may be connected together via a voltage interface to perform various analyses, such as the treatment of harmonics, or to acquire an understanding of design parameters such as capacity, runtime, and optimal asset utilization.
Robust transceivers to combat impulsive noise in powerline communications
(2014-05) Lin, Jing, active 2014; Evans, Brian L. (Brian Lawrence), 1965-
Future smart grid systems will intelligently monitor and control energy flows in order to improve the efficiency and reliability of power delivery. This monitoring and control requires low-power, low-cost and highly reliable two-way communications between customers and utilities. To enable these two-way communication links, powerline communication (PLC) systems are attractive because they can be deployed over existing outdoor and indoor power lines. Power lines, however, have traditionally been designed for one-directional power delivery and remain hostile environments for communication signal propagation. In particular, non-Gaussian noise that is dominated by asynchronous impulsive noise and periodic impulsive noise, is one of the primary factors that limit the communication performance of PLC systems. For my PhD dissertation, I propose transmitter and receiver methods to mitigate the impact of asynchronous impulsive noise and periodic impulsive noise, respectively, on PLC systems. The methods exploit sparsity and/or cyclostationarity of the noise in both time and frequency domains, and require no or minor training overhead prior to data transmission. Compared to conventional PLC systems, the proposed transceivers achieve dramatic improvement (up to 1000x) in coded bit error rates in simulations, while maintaining similar throughput.
The role of the smart grid in renewable energy progress : Abu Dhabi
(2012-12) Krishnan, Anirudh; Oden, Michael; Rai, Varun
Since the inception of the Masdar Initiative in 2006, the Emirate of Abu Dhabi has invested a considerable amount of resources to promote renewable sources of energy like solar and wind. With an aim of achieving 7% of its electricity from renewable sources by the year 2020, there is much that the emirate needs to do in order to reduce its reliance on hydrocarbons while still planning capacity for future electricity demand. This report explores the effectiveness of a smart grid infrastructure as a mechanism to afford the flexibility and functionality required to incorporate renewable energy sources into the electric grid, as well as leveraging a real-time data network to attain reductions in peak demand consumption. Specific regulatory structures that exist in Abu Dhabi's electric and telecommunications markets are evaluated to understand the role they will play in dealing with interoperability standards, privacy concerns, and consumer participation issues that influence the effective integration of smart grid into Abu Dhabi's energy future.
Sustainable microgrid and electric vehicle charging demand for a smarter grid
(2011-12) Bae, Sung Woo; Kwasinski, Alexis; Arapostathis, Aristotle; Driga, Mircea D.; Grady, William M.; Hebner, Robert E.
A “smarter grid” is expected to be more flexible and more reliable than traditional electric power grids. Among technologies required for the “smarter grid” deployment, this dissertation presents a sustainable microgrid and a spatial and temporal model of plug-in electric vehicle charging demand for the “smarter grid”. First, this dissertation proposes the dynamic modeling technique and operational strategies for a sustainable microgrid primarily powered by wind and solar energy resources. Multiple-input dc-dc converters are used to interface the renewable energy sources to the main dc bus. The intended application for such a microgrid is an area in which there is interest in achieving a sustainable energy solution, such as a telecommunication site or a residential area. Wind energy variations and rapidly changing solar irradiance are considered in order to explore the effect of such environmental variations to the intended microgrid. The proposed microgrid can be operated in an islanded mode in which it can continue to generate power during natural disasters or grid outages, thus improving disaster resiliency of the “smarter grid”. In addition, this dissertation presents the spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M/M/s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M/M/s queueing theory with the arrival rate of discharged vehicles. The first letter M of M/M/s indicates that discharged vehicles arrive at a charging station with the Poisson distribution. The second letter M denotes that the time to charge each EV is exponentially distributed, and the third letter s means that there are s identical charging pumps at a charging station. This mathematical model of charging demand may allow grid’s distribution planners to anticipate charging demand at a specific charging station.