Thermal Energy Storage for Solar Power Production

Solar energy is the most abundant persistent energy resource. It is also an intermittent one available for only a fraction of each day while the demand for electric power never ceases. To produce a significant amount of power at the utility scale, electricity generated from solar energy must be dispatchable and able to be supplied in response to variations in demand. This requires energy storage that serves to decouple the intermittent solar resource from the load and enables around-the-clock power production from solar energy. Practically, solar energy storage technologies must be efficient as any energy loss results in an increase in the amount of required collection hardware, the largest cost in a solar electric power system. Storing solar energy as heat has been shown to be an efficient, scalable, and relatively low-cost approach to providing dispatchable solar electricity. Concentrating solar power systems that include thermal energy storage (TES) use mirrors to focus sunlight onto a heat exchanger where it is converted to thermal energy that is carried away by a heat transfer fluid and used to drive a conventional thermal power cycle (e.g., steam power plant), or stored for later use. Several approaches to TES have been developed and can generally be categorized as either thermophysical (wherein energy is stored in a hot fluid or solid medium or by causing a phase change that can later be reversed to release heat) or thermochemical (in which energy is stored in chemical bonds requiring two or more reversible chemical reactions).

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications.

Minimizing residential distribution system operating costs through intelligently scheduled plug-in hybrid electric vehicle charging

Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more effcient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality.

Parameter estimation for transformer modeling

Large Power transformers, an aging and vulnerable part of our energy infrastructure, are at choke points in the grid and are key to reliability and security. Damage or destruction due to vandalism, misoperation, or other unexpected events is of great concern, given replacement costs upward of $2M and lead time of 12 months. Transient overvoltages can cause great damage and there is much interest in improving computer simulation models to correctly predict and avoid the consequences. EMTP (the Electromagnetic Transients Program) has been developed for computer simulation of power system transients. Component models for most equipment have been developed and benchmarked. Power transformers would appear to be simple. However, due to their nonlinear and frequency-dependent behaviors, they can be one of the most complex system components to model. It is imperative that the applied models be appropriate for the range of frequencies and excitation levels that the system experiences. Thus, transformer modeling is not a mature field and newer improved models must be made available. In this work, improved topologically-correct duality-based models are developed for three-phase autotransformers having five-legged, three-legged, and shell-form cores. The main problem in the implementation of detailed models is the lack of complete and reliable data, as no international standard suggests how to measure and calculate parameters. Therefore, parameter estimation methods are developed here to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, λ-i saturation characteristic, capacitive effects, and frequency dependency of winding resistance and core loss. Steady-state excitation, and de-energization and re-energization transients are simulated and compared with an earlier-developed BCTRAN-based model. Black start energization cases are also simulated as a means of model evaluation and compared with actual event records. The simulated results using the model developed here are reasonable and more correct than those of the BCTRAN-based model. Simulation accuracy is dependent on the accuracy of the equipment model and its parameters. This work is significant in that it advances existing parameter estimation methods in cases where the available data and measurements are incomplete. The accuracy of EMTP simulation for power systems including three-phase autotransformers is thus enhanced. Theoretical results obtained from this work provide a sound foundation for development of transformer parameter estimation methods using engineering optimization. In addition, it should be possible to refine which information and measurement data are necessary for complete duality-based transformer models. To further refine and develop the models and transformer parameter estimation methods developed here, iterative full-scale laboratory tests using high-voltage and high-power three-phase transformer would be helpful.

Development of system separation strategy using synchrophasor data

In the current market system, power systems are operated at higher loads for economic reasons. Power system stability becomes a genuine concern in such operating conditions. In case of failure of any larger component, the system may become stressed. These events may start cascading failures, which may lead to blackouts. One of the main reasons of the major recorded blackout events has been the unavailability of system-wide information. Synchrophasor technology has the capability to provide system-wide real time information. Phasor Measurement Units (PMUs) are the basic building block of this technology, which provide the Global Positioning System (GPS) time-stamped voltage and current phasor values along with the frequency. It is being assumed that synchrophasor data of all the buses is available and thus the whole system is fully observable. This information can be used to initiate islanding or system separation to avoid blackouts. A system separation strategy using synchrophasor data has been developed to answer the three main aspects of system separation: (1) When to separate: One class support machines (OC-SVM) is primarily used for the anomaly detection. Here OC-SVM was used to detect wide area instability. OC-SVM has been tested on different stable and unstable cases and it is found that OC-SVM has the capability to detect the wide area instability and thus is capable to answer the question of “when the system should be separated”. (2) Where to separate: The agglomerative clustering technique was used to find the groups of coherent buses. The lines connecting different groups of coherent buses form the separation surface. The rate of change of the bus voltage phase angles has been used as the input to this technique. This technique has the potential to exactly identify the lines to be tripped for the system separation. (3) What to do after separation: Load shedding was performed approximately equal to the sum of power flows along the candidate system separation lines should be initiated before tripping these lines. Therefore it is recommended that load shedding should be initiated before tripping the lines for system separation.

OUT-OF-STEP DETECTION BASED ON ZUBOV’S APPROXIMATION BOUNDARY METHOD

Disturbances in power systems may lead to electromagnetic transient oscillations due to mismatch of mechanical input power and electrical output power. Out-of-step conditions in power system are common after the disturbances where the continuous oscillations do not damp out and the system becomes unstable. Existing out-of-step detection methods are system specific as extensive off-line studies are required for setting of relays. Most of the existing algorithms also require network reduction techniques to apply in multi-machine power systems. To overcome these issues, this research applies Phasor Measurement Unit (PMU) data and Zubov’s approximation stability boundary method, which is a modification of Lyapunov’s direct method, to develop a novel out-of-step detection algorithm. The proposed out-of-step detection algorithm is tested in a Single Machine Infinite Bus system, IEEE 3-machine 9-bus, and IEEE 10-machine 39-bus systems. Simulation results show that the proposed algorithm is capable of detecting out-of-step conditions in multi-machine power systems without using network reduction techniques and a comparative study with an existing blinder method demonstrate that the decision times are faster. The simulation case studies also demonstrate that the proposed algorithm does not depend on power system parameters, hence it avoids the need of extensive off-line system studies as needed in other algorithms.

DC MICROGRID STABILIZATION THROUGH FUZZY CONTROL OF INTERLEAVED, HETEROGENEOUS STORAGE ELEMENTS

As microgrid power systems gain prevalence and renewable energy comprises greater and greater portions of distributed generation, energy storage becomes important to offset the higher variance of renewable energy sources and maximize their usefulness. One of the emerging techniques is to utilize a combination of lead-acid batteries and ultracapacitors to provide both short and long-term stabilization to microgrid systems. The different energy and power characteristics of batteries and ultracapacitors imply that they ought to be utilized in different ways. Traditional linear controls can use these energy storage systems to stabilize a power grid, but cannot effect more complex interactions. This research explores a fuzzy logic approach to microgrid stabilization. The ability of a fuzzy logic controller to regulate a dc bus in the presence of source and load fluctuations, in a manner comparable to traditional linear control systems, is explored and demonstrated. Furthermore, the expanded capabilities (such as storage balancing, self-protection, and battery optimization) of a fuzzy logic system over a traditional linear control system are shown. System simulation results are presented and validated through hardware-based experiments. These experiments confirm the capabilities of the fuzzy logic control system to regulate bus voltage, balance storage elements, optimize battery usage, and effect self-protection.

Influence of carbon enrichment on electrical conductivity and processing of polycarbosilane derived ceramic for MEMS applications

An analysis about the effect of carbon enrichment of allylhydridopolycarbosilane SMP10® with divinylbenzene (DVB) as a promising material for electrical conductive micro-electrical mechanical systems (MEMS) is presented. The liquid precursors can be micropipetted and cured in polytetrafluoroethylene (PTFE) molds to produce 14 mm diameter disc shaped samples. The effect of pyrolysis temperature and carbon content on the electrical conductivity is discussed. The addition of 28.7 wt.% of DVB was found to be the optimum amount. Carbon was preserved in the microstructure during pyrolysis and the ceramic yield increased from 77.5 to 80.5 wt.%. The electrical conductivity increased from 10−6 to 1 S/cm depending on the annealing temperature. Furthermore, the ceramic samples obtained with this composition were found to be in many cases crack free or with minimal cracks in contrast with the behavior of pure SMP10. Using the same process we demonstrate that also microsized ceramic samples can be produced.

Heterogeneous Collaborative Sensor Network for Electrical Management of an Automated House with PV Energy

In this paper we present a heterogeneous collaborative sensor network for electrical management in the residential sector. Improving demand-side management is very important in distributed energy generation applications. Sensing and control are the foundations of the “Smart Grid” which is the future of large-scale energy management. The system presented in this paper has been developed on a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead-acid batteries, controllable appliances and smart metering. Therefore, there is a large number of energy variables to be monitored that allow us to precisely manage the energy performance of the house by means of collaborative sensors. The experimental results, performed on a real house, demonstrate the feasibility of the proposed collaborative system to reduce the consumption of electrical power and to increase energy efficiency.

Pond head level control in a run-of-river hydro power plant using fuzzy controller

The run-of-river hydro power plant usually have low or nil water storage capacity, and therefore an adequate control strategy is required to keep the water level constant in pond. This paper presents a novel technique based on TSK fuzzy controller to maintain the pond head constant. The performance is investigated over a wide range of hill curve of hydro turbine. The results are compared with PI controller as discussed in [1].

PV self-consumption optimization with storage and Active DSM for the residential sector

With the rising prices of the retail electricity and the decreasing cost of the PV technology, grid parity with commercial electricity will soon become a reality in Europe. This fact, together with less attractive PV feed-in-tariffs in the near future and incentives to promote self-consumption suggest, that new operation modes for the PV Distributed Generation should be explored; differently from the traditional approach which is only based on maximizing the exported electricity to the grid. The smart metering is experiencing a growth in Europe and the United States but the possibilities of its use are still uncertain, in our system we propose their use to manage the storage and to allow the user to know their electrical power and energy balances. The ADSM has many benefits studied previously but also it has important challenges, in this paper we can observe and ADSM implementation example where we propose a solution to these challenges. In this paper we study the effects of the Active Demand-Side Management (ADSM) and storage systems in the amount of consumed local electrical energy. It has been developed on a prototype of a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead–acid batteries, controllable appliances and smart metering. We carried out simulations for long-time experiments (yearly studies) and real measures for short and mid-time experiments (daily and weekly studies). Results show the relationship between the electricity flows and the storage capacity, which is not linear and becomes an important design criterion.

Wind intermittency and CO2 reductions: the case of the spanish power system

Renewable energy sources are believed to reduce drastically greenhouse gas emissions that would otherwise be generated from fossil fuels used to generate electricity. This implies that a unit of renewable energy will replace a unit of fossil-fuel, with its CO2 emissions, on an equivalent basis (with no other effects on the grid). But, the fuel economy and emissions in the existing power systems are not proportional with the electricity production of intermittent sources due to cycling of the fossil fuel plants that make up the balance of the grid (i.e. changing the power output makes thermal units to operate less efficiently). This study focuses in the interactions between wind generation and thermal plants cycling, by establishing the levels of extra fuel use caused by decreased efficiencies of fossil back-up for wind electricity in Spain. We analyze the production of all thermal plants in 2011, studying different scenarios where wind penetration causes major deviations in programming, while we define a procedure for quantifying the carbon reductions by using emission factors and efficiency curves from the existing installations. The objectives are to discuss the real contributions of renewable energies to the environmental targets as well as suggest alternatives that would improve the reliability of future power systems.

Meter data management for smart monitoring power networks

The electrical power distribution and commercialization scenario is evolving worldwide, and electricity companies, faced with the challenge of new information requirements, are demanding IT solutions to deal with the smart monitoring of power networks. Two main challenges arise from data management and smart monitoring of power networks: real-time data acquisition and big data processing over short time periods. We present a solution in the form of a system architecture that conveys real time issues and has the capacity for big data management.

Analysis of the Iberian Power Forward Price Formation

The price formation of the Iberian Energy Derivatives Market-the power futures market-starting in July 2006, is assessed until November 2011, through the evolution of the difference between forward and spot prices in the delivery period (“ex-post forward risk premium”) and the comparison with the forward generation costs from natural gas (“clean spark spread”). The premium tends to be positive in all existing mechanisms (futures, Over-the-Counter and auctions for catering part of the last resort supplies). Since year 2011, the values are smaller due to regulatorily recognized prices for coal power plants. The power futures are strongly correlated with European gas prices. The spreads built with prompt contracts tend also to be positive. The biggest ones are for the month contract, followed by the quarter contract and then by the year contract. Therefore, gas fired generation companies can maximize profits trading with contracts of shorter maturity.

Systematic Method to Assess Small-Signal Stability of DC-Distributed Power-System-Architecture

The objective of this paper is to present a simplified method to analyze small-signal stability of a power system and provide performance metrics for stability assessment of a given power-system-architecture. The stability margins are stated utilizing a concept of maximum peak criteria (MPC), derived from the behavior of an impedance-based sensitivity function that provides a single number to state the robustness of the stability of a well-defined minor-loop gain. For each minor-loop gain, defined at every system interface, the robustness of the stability is provided as a maximum value of the corresponding sensitivity function. Typically power systems comprise of various interfaces and, therefore, in order to compare different architecture solutions in terms of stability, a single number providing an overall measure of the whole system stability is required. The selected figure of merit is geometric average of each maximum peak value within the system, combined with the worst case value of system interfaces.