Micro grid stability improvements by employing storage

Storage devices can be used in a power gird to store the excess energy when the energy production is high and the demand is low and utilize the stored energy when the produced energy cannot meet the high demands of the consumers. This thesis represents a micro grid consisting of a conventional synchronous generator, as well as renewable energy sources, energy storage, and loads in order to investigate the effective energy flow control and transient stability improvement by employing thermal storage. Thermal storage, unlike electrical one (such as battery) is more environmental friendly, has longer life span, and is more effective in power flow control. In this thesis, resistive and heat pump type thermal storage are proposed and its stability effects on micro grids are evaluated. A suitable model is developed for the thermal storage and the grid’s stability analysis is adopted by using linearization methods. Consequently, by designing an optimal controller for the storage the stability of the micro grid is improved as verified through the simulations

Optimization of the Performance of Micro Hydro-Turbines for Electricity Generation

Rural electrification has long been the most important topic on the development agenda of many countries. The needs for power supplies to rural areas increased significantly in the past decades. Extending electricity grids to rural areas is of a very high initial cost and is not viable economically. Micro hydroelectric power plants provide a good economical solution, which is also environmentally very friendly. The current study concentrates on selecting and optimizing a suitable cross-flow micro-turbine to be used in micro hydroelectric power plants. Cross-flow turbines are in general of simple structure, low cost, easy to fabricate and of modest efficiency. The main purpose of the present work is to optimize the performance of a selected turbine by establishing the optimal turbine’s design parameters. A complete analysis of the internal flow, which is of turbulent, two-phase and three dimensional in nature, was undertaken by simulating it using various CFD simulation codes. This study reports on the flow simulation using ANSYS CFX with a two-phase flow model, water-air free surface model and shear stress transport (SST) turbulence model. Prediction velocity and pressure fields of inside the turbine are, subsequently, used to characterize the turbine performance for different geometric parameters including the number of runner blades, the angle of attack, the ratio of inner to outer diameter, the nozzle profile, the blade profile, the nozzle throat width, the nozzle to runner blades width and the runner blades width to outer runner diameter. The results revealed the highly complex nature of the flow and provided a very good insight to the flow structure and performance optimization parameters.Kurdistan Regional Governmen

A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets

Microgrids are going to be used in future intelligent grids as a promising technology to enable widespread utilization of renewable energy sources in a highly efficient and reliable manner. It is known that reconfiguration of micro-grids, using tie-line and sectionalizing switches, can provide more operational flexibility. Additionally, coordinated scheduling of flexible loads and energy storage systems can play an important role in the optimal scheduling of micro-grids; thus lowering the costs. This paper proposes an optimal bidding strategy for a micro-grid in day-ahead and real-time markets, based on AC power flow model, considering the hourly reconfiguration of the micro-grid. Fuel cell-based hydrogen energy storage and multiple shiftable loads are considered in the proposed method according to the load’s activity schedule. A reconfigurable micro-grid incorporates energy production and consumption of its local components to trade power in both day-ahead and real-time markets in order to maximize its profit as a private entity. The bidding problem faces issues due to the high level of uncertainties, consisting of wind power generation and electric load as well as variations of market prices. A hybrid two-stage bi-level optimization model is proposed to manage such uncertainties so that wind power, load demand, and day-ahead market prices are handled through scenario-based stochastic programming, and an information gap decision theory is applied to model the uncertainty of real-time market prices under two strategies, namely risk-seeker and risk-averse. The numerical simulation results confirm the effectiveness of the proposed model

Active and Reactive Power Control of Flexible Loads for Distribution-Level Grid Services

Electric vehicle (EV) charging/discharging can take place in any P-Q quadrants, which means EVs could provide reactive power at any state-of-charge (SOC). This dissertation shows four-quadrant operation of EVs and aggregation of EVs for support of grid operations. First, this work develops hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed EVs incorporating distribution grid level constraints. This work demonstrates benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5,000). Case studies demonstrate that, in constrained distribution grids, coordinated charging reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to the unity power factor. Next, this work utilizes detailed EV battery model that could be leveraged for its four-quadrant operations. Then, the developed work coordinates the operations of EVs and distribution feeder to support voltage profile on the grid in real time. The grid level problem is devised as a distribution optimal power flow model to compute voltage regulation signal to dispatch active/reactive power set points of individual EVs. The efficacy of the developed models are demonstrated by using a LV secondary feeder, where EVs\u27 operating in all four quadrants are shown to compensate the feeder voltage fluctuations caused by daily time varying residential loads, while honoring other operational constraints of the feeder. Furthermore, a novel grid application, called virtual power plant (VPP), is developed. Traditional nonlinear power flow problems are nonconvex, hence, time consuming to solve. In order to be used in real time simulation in VPP, an efficient linearized optimal power flow model is developed. This linearization method is used to solve a 534-bus power system with 3 VPPs in real-time. This work also implements VPP scheduling in real-time using OPAL-RT\u27s simulator in hardware-in-the-loop (HIL), where the loads are emulated using micro-controller devices

Refueling: Preventing wire degradation due to electromigration

Electromigration is a major source of wire and via failure. Refueling undoes EM for bidirectional wires and power/ground grids-some of a chip's most vulnerable wires. Refueling exploits EM's self-healing effect by balancing the amount of current flowing in both directions of a wire. It can significantly extend a wire's lifetime while reducing the chip area devoted to wires.Peer ReviewedPostprint (published version

Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability

Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow control. This paper suggests a novel algorithm for learning the topology of distribution grid and estimating impedances of the operational lines with minimal observational requirements - it provably reconstructs topology and impedances using voltage and injection measured only at the terminal (end-user) nodes of the distribution grid. All other (intermediate) nodes in the network may be unobserved/hidden. Furthermore no additional input (e.g., number of grid nodes, historical information on injections at hidden nodes) is needed for the learning to succeed. Performance of the algorithm is illustrated in numerical experiments on the IEEE and custom power distribution models