Philip Taylor

Voltage Control Techniques for Electrical Distribution Networks Including Distributed Generation

For the last three decades, a large scale integration of distributed generation (DG) is beginning to change the electrical distribution network from passive to active. Consequently, technical difficulties are created by significant impacts generated by DGs with voltage variation being the dominant effect. This paper presents a comprehensive review of voltage control techniques on electrical distribution networks connected with DG, recommendations are provided in terms of enhancing network voltage stability and maximising the DG utilisation.

The Autonomic Power System - Network operation and control beyond smart grids

A wide range of applications are being researched and developed within the Smart Grid community, such as voltage control, thermal constraint management, dynamic line ratings and automated reconfiguration. Typically, the current approach is to develop piecemeal automation applied to small sections of legacy networks under current market, commercial and regulatory regimes. The challenges of future energy networks are the anticipated uncertainty and complexity within them. This includes uncertainty in the equipment, configurations and control functionality required married with uncertainty in the participation of consumers through demand side technologies and the uptake of electric vehicles and microgeneration technologies; while complexity is engendered in managing the vast number of interactions within such a system. The authors are developing the concept of the Autonomic Power System which provides flexible and adaptable control through fully distributed intelligence and control. Fundamental research in intelligent systems and network control will deliver a truly integrated self-controlling, self-optimising, self-healing and self-protecting electricity network. This paper outlines the vision, architecture and initial control techniques which will deliver the Autonomic Power System.

Innovative Application of Demand Side Management to Power Systems

Demand side management is normally used to reduce the total load demand of power systems during periods of peak demands in order to maintain the security of the system. It has been used for this purpose in the past 10 years so that utilities can defer the need of reinforcing their networks as well as the need of increasing the capacity of the generators. Research has been carried out in order to identify additional functions and benefits that demand side management can bring to end users and utilities. This paper presents the potential of using demand side management to solve voltage rise problems on distributed networks integrated with a large number of distributed generators

Zoning Evaluation for Improved Coordinated Automatic Voltage Control

Hierarchically structured automatic voltage control (AVC) architecture has attracted increased interest as networks operate closer to their capacity limits. Hierarchical AVC enables wide-area coordinated voltage regulation (CVR). Due to the inherent complexity of the task, it is based on reduced control models, i.e., simplified models of the system suitable for voltage control. It is a fact however that a single reduced control model (static RCM) cannot be optimal for all network configurations and operating conditions. In pursuit of an improved CVR, this paper investigates the applicability of zoning methodologies in adaptively determined RCM. It further argues that the selection of a zoning methodology affects not only the CVR operation, but also its robustness to erroneous data and proposes a comprehensive generic framework for evaluating its performance. Lastly, it extends and evaluates several zoning-based control model reduction methodologies: namely, hierarchical clustering employing two different proximity metrics, spectral k-way and fuzzy C-means, on both static and adaptive schemes.

A Probabilistic Method Combining Electrical Energy Storage and Real-Time Thermal Ratings to Defer Network Reinforcement

When a primary substation reaches its capacity limit, the standard solution is to reinforce the network with additional circuits. Under the right conditions, the required additional peak capacity can be provided by energy storage systems (ESS), real-time thermal ratings (RTTR) or a combination of the two. We present a probabilistic method for calculating the size of an electrical energy storage system for a demand peak shaving application. The impact of both power and energy capacity are considered, along with the reliability of the energy storage and the existing overhead lines. We also consider the combination of energy storage and RTTR - taking advantage of the inherent variability in power line rating as a result of changing weather conditions - for enhancing reliability, deferring conventional reinforcement, and increasing the availability of energy storage to participate in commercial service markets. The method is demonstrated in a case study on a network with an ongoing 6-MW/10-MWh ESS innovation project.

Applying Wind Simulations for Planning and Operation of Real-Time Thermal Ratings

Real-time thermal ratings (RTTRs) are an emerging technology that allows the rating of electrical conductors to be estimated using real-time local weather observations. In many cases this leads to a very significant (typically 50%–100%) increase in rating with respect to conventional approaches. Conductor rating is heavily influenced by wind speed and direction. Consequently, in this paper, computational wind simulations commonly employed by the wind energy industry have been applied to inform rating estimation during network planning and operation. This provides an exciting opportunity to allow the identification of determining conductor spans to inform network designers of the rating potential of different conductor routes to estimate the additional wind energy that could be accommodated through the enhanced line rating and to allow intelligent placement of the monitoring equipment required to implement RTTR. The wind simulation data were also used to allow more accurate estimation of conductor ratings during operation. Two case studies taken from actual trial sites in the U.K. are presented to demonstrate that these techniques can provide a real world benefit.

Robust Scheduling Scheme for Energy Storage to Facilitate High Penetration of Renewables

This paper presents a robust scheduling scheme for energy storage systems (ESSs) deployed in distribution networks to facilitate high penetrations of renewable energy sources (RES). This scheme schedules the charging and discharging of an ESS cognizant of state-of-charge (SoC) limits, transmission line real time thermal ratings (RTTR), and voltage constraints. Robust optimization (RO) has been adopted to deal with the uncertainty of RES output, load, and RTTR. Two methods have been introduced to estimate the tradeoff between the cost and the probability of constraint violations. The proposed scheduling scheme is tested on the IEEE 14 and 118 busbar networks with real load, generation, and RTTR profiles through Monte Carlo simulation (MCS). Test results show that the proposed scheme is able to minimize or curtail the probability of constraint violation to a desired level. In contrast, classical optimal power flow (OPF) approaches which do not consider uncertainty, when coupled with RTTR and ESS, result in a low PoS. At the same time, compared to conservative OPF approaches, the proposed scheme reduces the power and energy requirement of ESS.

Use of standby generation for reduction of transmission network charges for half-hourly metered customers

Electricity customers on the GB network pay transmission network use of service (TNUoS) charges. For half-hourly metered (HHM) customers there are “Triad” demand charges which apply to three half-hour periods per year. The periods represent peak system demand and are not known in advance. These (HHM) customers can reduce their Triad charge by minimizing their demand during periods which have a high likelihood of being a Triad. Suppliers and energy service companies can provide warnings of these periods. Many commercial customers have on-site emergency generators to ensure the continuity of critical supplies in case of a supply failure which could be engaged to reduce Triad demand. This paper describes a model of the costs of transmission charges (Triad), distribution network use-of-service charges (DUoS) and energy charges for half-hourly (HH) metered customers. It models the effect of using a standby generator for reducing these costs and calculates the fuel cost and the quantity of CO2 emissions. The model is applied a case study of a building at Newcastle University in which the use of standby generation for Triad avoidance is compared against the existing costs. The cost of diesel fuel consumption is also considered so that the net benefit of using standby generation for Triad avoidance can be determined.

CFD analysis of the thermal state of an overhead line conductor

At present commercial CFD packages such as Fluent, ANSYS CFX, and Star-CD are widely used for investigation of heat and mass transfer processes in various fields of engineering. These codes can also be successfully applied to estimate the thermal state of major components of electrical distribution networks, such as overhead lines, underground cables and transformers. This paper presents some results obtained from numerical modelling of the temperature field in the Lynx overhead conductor in both cross and parallel wind conditions using 2-D and 3-D CFD models. The CFD results obtained demonstrate that for an applied load of 433 A and considering the summer rating (Lynx conductors ER P27 [1]) the maximum temperature in the conductor is considerably lower (16 degrees) than the prescribed design conductor temperature. This indicates that there is headroom for increasing the ampacity of the conductor.

Network State-Based Algorithm Selection for Power Flow Management Using Machine Learning

This paper demonstrates that machine learning can be used to create effective algorithm selectors that select between power system control algorithms depending on the state of a network, achieving better performance than always using the same algorithm for every state. Also presented is a novel method for creating algorithm selectors that consider two objectives. The method is used to develop algorithm selectors for power flow management algorithms on versions of the IEEE 14- and 57-bus networks, and a network derived from a real distribution network. The selectors choose from within a diverse set of power flow management algorithms, including those based on constraint satisfaction, optimal power flow, power flow sensitivity factors, and linear programming. The network state-based algorithm selectors offer performance benefits over always using the same power flow management algorithm for every state, in terms of minimizing the number of overloads while also minimizing the curtailment applied to generators.