An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Review of dynamic line rating systems for wind power integration

When a wind power system is connected to a network point there is a limit of power generation based on the characteristics of the network and the loads connected to it. Traditionally, transmission line limits are estimated conservatively assuming unfavourable weather conditions (high ambient temperature, full sun and low wind speed). However, the transmission capacity of an overhead line increases when wind speed is high, due to the cooling caused by wind in the distribution lines. Dynamic line rating (DLR) systems allow monitoring real weather conditions and calculating the real capacity of lines. Thus, when planning wind power integration, if dynamic line limits are considered instead of the conservative and static limits, estimated capacity increases. This article reviews all technologies developed for real-time monitoring during the last thirty years, as well as some case studies around the world, and brings out the benefits and technical limitations of employing dynamic line rating on overhead lines. Further, the use of these DLR systems in wind integration is reviewed.This work is financially supported by the Ministerio de Economía y Competitividad under the project DPI2013-44502-R and the Eusko Jaurlaritza under the project SAI12/103

Probabilistic real-time thermal rating forecasting for overhead lines by conditionally heteroscedastic auto-regressive models

Conventional approaches to forecasting of real-time thermal ratings (RTTRs) provide only single point estimates with no indication of the size or distribution of possible errors. This paper describes weather based methods to estimate probabilistic RTTR forecasts for overhead lines which can be used by a system operator within a chosen risk policy with respect to probability of a rating being exceeded. Predictive centres of weather conditions are estimated as a sum of residuals predicted by a suitable auto-regressive model and temporal trends fitted by Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of recent wind direction observations within two hours. A technique of minimum continuous ranked probability score estimation is used to estimate predictive distributions. Numerous RTTRs for a particular span are generated by a combination of the Monte Carlo method where weather inputs are randomly sampled from the modelled predictive distributions at a particular future moment and a thermal model of overhead conductors. Kernel density estimation is then used to smooth and estimate the percentiles of RTTR forecasts which are then compared with actual ratings and discussed alongside practical issues around use of RTTR forecasts

Performance Enhancement of Power System Operation and Planning through Advanced Advisory Mechanisms

abstract: This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable resources. The primary purpose of this research is to identify soft spots of the contemporary industry practices and propose innovative algorithms, methodologies, and tools to improve economics and reliability in power systems. First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies. The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

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

Dynamic conductor ratings: annealing properties of homogenous conductors across varying climatic conditions

Static line ratings are currently utilised by electrical supply authorities to provide a safe and conservative rating for their overhead network. This in turns provides energy security to the customer and longevity for the conductor. Due to current economic environments, energy supply authorities are starting to consider the implementation of dynamic line ratings (DLRs). The dissertation’s main objective is to analyse the available methodologies for modelling the heat balance equation (HBE) in order to provide Ergon Energy Corporation Limited (EECL) the means to implement dynamic ratings. Research has proven that a DLR exploits the available weather parameters and temperature levels in order to increase or decrease the networks ampacity, hence a DLR is typically established by adjusting the convective losses. This has become an area of interest for EECL as they continue to investigate ways to reduce both their capital and operational expenditure, whilst continuing to be an explorative and innovative company. The initial aim of scrutinising the HBE methodology is to determine which application is most suitable for EECL. This was identified as a critical task as the Australian standard AS/NZS 7000:2010 directs the reader directly to IEC/TR 61597-1995, which is dramatically different to that which is provided by ESAA D(b)5 1988, the currently methodology utilised by EECL. The results of this sensitivity test reiterated that the existing process provided by ESAA D(b)5 is in fact satisfactory as it is more superior to the IEC/TR 61597, hence eliminating EECL from remodelling there ratings process. The completion of the sensitivity analysis returned results that highlighted specific characteristics which can be used to adjust the ampacity of the network. These components are; ambient temperature; wind speed; and wind approach angle. Such knowledge becomes beneficial as EECL has access to the Bureau of Meteorology’s (BOM) historical weather data, for which a statistical analysis can provide a means of predicting the types of weather conditions expected over the duration of the dynamic period. Associated with DLRs is the inherent risk of conductor annealing and increased conductor sag. Understanding the severity of the risks, it has been identified that the lifespan of the conductor was required to be determined. Calculating this is problematic for EECL as there are minimal records of conductor operating temperatures and high temperature exposures. To overcome this problem, a dynamic conductor ratings model was created. This model stipulates that if the current tensile strength of the conductor is unknown, it is to be sampled and tested. The results of this test will provide an expected tensile strength that can be used to back engineer the residual lifespan, hence allowing for the level of risk in a DLR to be quantified. The impact of uprating a conductor with respects to DLRs, as identified in the dissertation, has the potential to provide great financial benefits and more flexibility to the network. It also has the potential to place the asset and community at serious risk. The results and outcomes of this dissertation have the potential to assist plant rating engineers, asset managers and network planners in their understanding and application of DLRs, such that they will be able to identify portions of the network which as safe to uprate and portions which may require de-rating. It is also expected that by highlighting the risks involved, it will encourage EECL to begin monitoring the lifespan of the network, hence allowing them to gain a better knowledge of the life expectancy of a network and its available ampacity