Power system real-time thermal rating estimation

This Thesis describes the development and testing of a real-time rating estimation algorithm developed at Durham University within the framework of the partially Government-funded research and development project “Active network management based on component thermal properties”, involving Durham University, ScottishPower EnergyNetworks, AREVA-T&D, PB Power and Imass. The concept of real time ratings is based on the observation that power system component current carrying capacity is strongly influenced by variable environmental parameters such as air temperature or wind speed. On the contrary, the current operating practice consists of using static component ratings based on conservative assumptions. Therefore, the adoption of real-time ratings would allow latent network capacity to be unlocked with positive outcomes in a number of aspects of distribution network operation. This research is mainly focused on facilitating renewable energy connection to the distribution level, since thermal overloads are the main cause of constraints for connections at the medium and high voltage levels. Additionally its application is expected to facilitate network operation in case of thermal problems created by load growth, delaying and optimizing network reinforcements. The work aims at providing a solution to part of the problems inherent in the development of a real-time rating system, such as reducing measurements points, data uncertainty and communication failure. An extensive validation allowed a quantification of the performance of the algorithm developed, building the necessary confidence for a practical application of the system developed

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Stochastic Model for Power Grid Dynamics

We introduce a stochastic model that describes the quasi-static dynamics of an electric transmission network under perturbations introduced by random load fluctuations, random removing of system components from service, random repair times for the failed components, and random response times to implement optimal system corrections for removing line overloads in a damaged or stressed transmission network. We use a linear approximation to the network flow equations and apply linear programming techniques that optimize the dispatching of generators and loads in order to eliminate the network overloads associated with a damaged system. We also provide a simple model for the operator's response to various contingency events that is not always optimal due to either failure of the state estimation system or due to the incorrect subjective assessment of the severity associated with these events. This further allows us to use a game theoretic framework for casting the optimization of the operator's response into the choice of the optimal strategy which minimizes the operating cost. We use a simple strategy space which is the degree of tolerance to line overloads and which is an automatic control (optimization) parameter that can be adjusted to trade off automatic load shed without propagating cascades versus reduced load shed and an increased risk of propagating cascades. The tolerance parameter is chosen to describes a smooth transition from a risk averse to a risk taken strategy...Comment: framework for a system-level analysis of the power grid from the viewpoint of complex network

Overview of methods to analyse dynamic data

This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

Active Management of Distributed Generation based on Component Thermal Properties

Power flows within distribution networks are expected to become increasingly congested with the proliferation of distributed generation (DG) from renewable energy resources. Consequently, the size, energy penetration and ultimately the revenue stream of DG schemes may be limited in the future. This research seeks to facilitate increased renewable energy penetrations by utilising power system component thermal properties together with DG power output control techniques. The real-time thermal rating of existing power system components has the potential to unlock latent power transfer capacities. When integrated with a DG power output control system, greater installed capacities of DG may be accommodated within the distribution network. Moreover, the secure operation of the network is maintained through the constraint of DG power outputs to manage network power flows. The research presented in this thesis forms part of a UK government funded project which aims to develop and deploy an on-line power output control system for wind-based DG schemes. This is based on the concept that high power flows resulting from wind generation at high wind speeds could be accommodated since the same wind speed has a positive effect on component cooling mechanisms. The control system compares component real-time thermal ratings with network power flows and produces set points that are fed back to the DG for implementation. The control algorithm comprises: (i) An inference engine (using rule-based artificial intelligence) that decides when DG control actions are required; (ii) a DG set point calculator (utilising predetermined power flow sensitivity factors) that computes updated DG power outputs to manage distribution network power flows; and (iii) an on-line simulation tool that validates the control actions before dispatch. A section of the UK power system has been selected by ScottishPower EnergyNetworks to form the basis of field trials. Electrical and thermal datasets from the field are used in open loop to validate the algorithms developed. The loop is then closed through simulation to automate DG output control for increased renewable energy penetrations

A Comparison of Real Time Thermal Rating Systems in the U.S. and the UK

Real-Time Thermal Rating is a smart grid technology that allows the rating of electrical conductors to be increased based on local weather conditions. Overhead lines are conventionally given a conservative, constant seasonal rating based on seasonal and regional worst case scenarios rather than actual, say, local hourly weather predictions. This paper provides a report of two pioneering schemes—one in the United States of America and one in the United Kingdom—in which Real-Time Thermal Ratings have been applied. Thereby, we demonstrate that observing the local weather conditions in real time leads to additional capacity and safer operation. Secondly, we critically compare both approaches and discuss their limitations. In doing so, we arrive at novel insights which will inform and improve future Real-Time Thermal Rating projects

Power System Operation Planning Considering Dynamic Line Rating Uncertainty

The restructuring of power systems and wider introduction of renewable energy sources in the recent years is placing a greater stress on the transmission system. Yet, transmission system is paramount for the reliable, secure and economic operation of power systems. However, modern transmission systems often have insufficient capacity, leading to bottlenecks, congestions and spillage of renewable energy, while their expansion is generally expensive, complicated and time consuming. As an alternative to the transmission expansion, dynamic line rating technologies allows to utilize latent capacity of transmission lines through the use of measurements or forecasts of weather parameters. However, as the forecasts of the weather parameters are inherently uncertain, the estimates of transmission capacity also become uncertain, and must be addressed accordingly. This thesis investigates the impacts of dynamic line rating forecast uncertainty in power system operational planning problems. Thus, the thesis aims at developing mathematical models for the management of such uncertainty to ensure secure and effective operation of power systems. In order to achieve the above objective, firstly, stochastic models for the dynamic line rating are developed that allow to consider thermal dynamics of the conductor in the presence of uncertain weather forecasts. The models are entirely data-based and provide a risk-averse method of controlling conductor temperature in operational planning problems. Furthermore, the models allow to control both the probability of occurrence and the magnitude of the thermal overloading. Secondly, an analysis of uncertain factors and their interactions in power system operational planning is performed using the coherent risk measure framework. Additionally, a novel modelling approach for the uncertain renewable energy sources in operational planning problems is proposed. Then, coherent reformulations of uncertain constraints are developed and integrated into day-ahead unit commitment problem. Finally, the benefits of managing risk in operational planning problems using coherent risk measures are demonstrated in comprehensive case studies

Direct monitoring methods of overhead line conductor temperature

The concept of conductor temperature monitoring has gained in importance with development of advanced electricity networks whose main objective is to increase the capacity, efficiency and reliability of modern power systems. The methods applied to the conductor temperature monitoring of overhead lines can be roughly classified as direct and indirect. In direct methods for conductor temperature monitoring, the temperature is measured directly or by measuring a particular conductor parameter, which is temperature dependent such as sag, tension, conductor resistance, conductor distance from the ground, etc. In indirect methods for the conductor temperature monitoring, the conductor temperature is obtained by applying a specific mathematical model that as an input uses the measured values of weather parameters and line current. Basically, this paper focuses on the issues of direct methods for conductor temperature monitoring, thus providing an analysis of advantages and disadvantages of each method

Dynamic rating management of overhead transmission lines operating under multiple weather conditions

Integration of a large number of renewable systems produces line congestions, resulting in a problem for distribution companies, since the lines are not capable of transporting all the energy that is generated. Both environmental and economic constraints do not allow the building new lines to manage the energy from renewable sources, so the efforts have to focus on the existing facilities. Dynamic Rating Management (DRM) of power lines is one of the best options to achieve an increase in the capacity of the lines. The practical application of DRM, based on standards IEEE (Std.738, 2012) and CIGRE TB601 (Technical Brochure 601, 2014) , allows to find several deficiencies related to errors in estimations. These errors encourage the design of a procedure to obtain high accuracy ampacity values. In the case of this paper, two methodologies have been tested to reduce estimation errors. Both methodologies use the variation of the weather inputs. It is demonstrated that a reduction of the conductor temperature calculation error has been achieved and, consequently, a reduction of ampacity error.This research was funded by the Spanish Government AND FEDER funds under the R+D initiative RETOS COLABORACIÓN 2015” with reference RETOS COLABORACIÓN RTC-2015-3795-3 and Spanish R+D initiative with reference ENE2013-42720-R