Demand Reduction and Responsive Strategies for Underground Mining

This thesis presents a demand reduction and responsive strategy for underground mining operations. The thesis starts with a literature review and background research on global energy, coal mining and the energy related issues that the mining industry face everyday. The thesis then goes on to discuss underground mine electrical power systems, data acquisition, load profiling, priority ranking, load shedding and demand side management in mining. Other areas presented in this thesis are existing energy reduction techniques, including: high efficiency motors, motor speed reduction and low energy lighting. During the thesis a data acquisition system was designed and installed at a UK Coal colliery and integrated into the mines existing supervisory control and data acquisition (SCADA) system. Design and installation problems were overcome with the construction of a test meter and lab installation and testing. A detailed explanation of the system design and installation along with the data analysis of the data from the installed system. A comprehensive load profile and load characterisation system was developed by the author. The load profiling system is comprehensive allows the definition of any type of load profile. These load profiles are fixed, variable and transient load types. The loads output and electrical demand are all taken into consideration. The load characterisation system developed is also very comprehensive. The LC MATRIX is used with the load profiles and the load characteristics to define off-line schedules. A set of unique real-time decision algorithms are also developed by the author to operate the off-line schedules within the desired objective function. MATLAB Simulation is used to developed and test the systems. Results from these test are presented. Application of the developed load profiling and scheduling systems are applied to the data collected from the mine, the results of this and the cost savings are also presented

Modeling and Real-Time Scheduling of DC Platform Supply Vessel for Fuel Efficient Operation

DC marine architecture integrated with variable speed diesel generators (DGs) has garnered the attention of the researchers primarily because of its ability to deliver fuel efficient operation. This paper aims in modeling and to autonomously perform real-time load scheduling of dc platform supply vessel (PSV) with an objective to minimize specific fuel oil consumption (SFOC) for better fuel efficiency. Focus has been on the modeling of various components and control routines, which are envisaged to be an integral part of dc PSVs. Integration with photovoltaic-based energy storage system (ESS) has been considered as an option to cater for the short time load transients. In this context, this paper proposes a real-time transient simulation scheme, which comprises of optimized generation scheduling of generators and ESS using dc optimal power flow algorithm. This framework considers real dynamics of dc PSV during various marine operations with possible contingency scenarios, such as outage of generation systems, abrupt load changes, and unavailability of ESS. The proposed modeling and control routines with real-time transient simulation scheme have been validated utilizing the real-time marine simulation platform. The results indicate that the coordinated treatment of renewable based ESS with DGs operating with optimized speed yields better fuel savings. This has been observed in improved SFOC operating trajectory for critical marine missions. Furthermore, SFOC minimization at multiple suboptimal points with its treatment in the real-time marine system is also highlighted

Resilience-driven planning and operation of networked microgrids featuring decentralisation and flexibility

High-impact and low-probability extreme events including both man-made events and natural weather events can cause severe damage to power systems. These events are typically rare but featured in long duration and large scale. Many research efforts have been conducted on the resilience enhancement of modern power systems. In recent years, microgrids (MGs) with distributed energy resources (DERs) including both conventional generation resources and renewable energy sources provide a viable solution for the resilience enhancement of such multi-energy systems during extreme events. More specifically, several islanded MGs after extreme events can be connected with each other as a cluster, which has the advantage of significantly reducing load shedding through energy sharing among them. On the other hand, mobile power sources (MPSs) such as mobile energy storage systems (MESSs), electric vehicles (EVs), and mobile emergency generators (MEGs) have been gradually deployed in current energy systems for resilience enhancement due to their significant advantages on mobility and flexibility. Given such a context, a literature review on resilience-driven planning and operation problems featuring MGs is presented in detail, while research limitations are summarised briefly. Then, this thesis investigates how to develop appropriate planning and operation models for the resilience enhancement of networked MGs via different types of DERs (e.g., MGs, ESSs, EVs, MESSs, etc.). This research is conducted in the following application scenarios: 1. This thesis proposes novel operation strategies for hybrid AC/DC MGs and networked MGs towards resilience enhancement. Three modelling approaches including centralised control, hierarchical control, and distributed control have been applied to formulate the proposed operation problems. A detailed non-linear AC OPF algorithm is employed to model each MG capturing all the network and technical constraints relating to stability properties (e.g., voltage limits, active and reactive power flow limits, and power losses), while uncertainties associated with renewable energy sources and load profiles are incorporated into the proposed models via stochastic programming. Impacts of limited generation resources, load distinction intro critical and non-critical, and severe contingencies (e.g., multiple line outages) are appropriately captured to mimic a realistic scenario. 2. This thesis introduces MPSs (e.g., EVs and MESSs) into the suggested networked MGs against the severe contingencies caused by extreme events. Specifically, time-coupled routing and scheduling characteristics of MPSs inside each MG are modelled to reduce load shedding when large damage is caused to each MG during extreme events. Both transportation networks and power networks are considered in the proposed models, while transporting time of MPSs between different transportation nodes is also appropriately captured. 3. This thesis focuses on developing realistic planning models for the optimal sizing problem of networked MGs capturing a trade-off between resilience and cost, while both internal uncertainties and external contingencies are considered in the suggested three-level planning model. Additionally, a resilience-driven planning model is developed to solve the coupled optimal sizing and pre-positioning problem of MESSs in the context of decentralised networked MGs. Internal uncertainties are captured in the model via stochastic programming, while external contingencies are included through the three-level structure. 4. This thesis investigates the application of artificial intelligence techniques to power system operations. Specifically, a model-free multi-agent reinforcement learning (MARL) approach is proposed for the coordinated routing and scheduling problem of multiple MESSs towards resilience enhancement. The parameterized double deep Q-network method (P-DDQN) is employed to capture a hybrid policy including both discrete and continuous actions. A coupled power-transportation network featuring a linearised AC OPF algorithm is realised as the environment, while uncertainties associated with renewable energy sources, load profiles, line outages, and traffic volumes are incorporated into the proposed data-driven approach through the learning procedure.Open Acces

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Minimize the load reduction considering the activities control of the generators and phase distance

This study shows how to calculate the minimum load that needs to be reduced to restore the frequency to the specified threshold. To implement this problem, the actual operation of the electricity system in the event of a generator outage is considered. The main idea of this method is to use the power balance equation between the generation and the load with different frequency levels. In all cases of operating the electrical system before and after the generator outage, the reserve capacity of other generators is considered in each generator outage situation. The reduced load capacity is calculated based on the reciprocal phase angle sensitivity or phase distance. This makes the voltage phase angle and voltage value quality of recovery nodes better. The standard IEEE 9-generator 37-bus test scheme was simulated to show the result of the proposed technique

Portuguese transmission grid incidents risk assessment

Documento confidencial. Não pode ser disponibilizado para consultaTese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Enhanced AC Quasi-steady State Cascading Failure Model for Grid Vulnerability Analysis under Wind Uncertainty

This paper presents several enhancements on a mixed OPF-stochastic cascading failure model to study the impacts of renewable energy resource uncertainty on grid vulnerability. The improved quasi-steady state (QSS) cascading failure model incorporates AC power flow calculations thus allowing us to simulate voltage-related failures in the grid. The under-voltage load shedding (UVLS) relays are modeled along with a stochastic time-inverse overload relay to accurately simulate the protective system response. In addition, more realistic assumptions are considered in the modeling of wind power penetration using geographical information of grid topology and wind potential map for a given geographical area. The effectiveness of the proposed framework is evaluated on a 500-bus synthetic network developed based on the footprints of South Carolina. The enhanced model allows us to more accurately simulate cascades in the power system with high penetration of erratic renewables and identify weak points

Techno-economic impacts of automatic undervoltage load shedding under emergency

Different schemes for voltage control under emergency are adopted in different jurisdictions around the world. While some features, such as Automatic Voltage Regulation (AVR), are common in all countries, for what concerns undervoltage load shedding (UVLS), to contrast voltage instability or collapse, different schemes are adopted. Most US transmission system operators (TSOs) adopt automatic UVLS schemes, with different capabilities and settings while TSOs in EU usually do not implement automatic UVLS but leave the decisions to the control room operators. The two options may lead to different impacts in terms of trajectory and final status of the transmission grid under emergency, with different unserved energy. In this paper we analyze the impacts from a technical and economic perspective, modeling the grid behavior with different UVLS schemes (none, manual and automatic). The comparison between the different schemes is done resorting to the Incident Response System (IRS), a software tool developed by the authors in the EU-FP7 SESAME project. An illustrative example to a realistic test case is presented and discussed. This paper shows that automatic UVLS is superior to Manual UVLS, from both technical and economic point of view, due to the fast evolution of voltage collapse phenomena and insufficient time for system operators' manual reaction. The benefits of the scheme involving the automatic UVLS can be then compared with the investment costs of equipping the network with those devices

Techno-economic impacts of automatic undervoltage load shedding under emergency

© 2015 Elsevier B.V. All rights reserved. Different schemes for voltage control under emergency are adopted in different jurisdictions around the world. While some features, such as Automatic Voltage Regulation (AVR), are common in all countries, for what concerns undervoltage load shedding (UVLS), to contrast voltage instability or collapse, different schemes are adopted. Most US transmission system operators (TSOs) adopt automatic UVLS schemes, with different capabilities and settings while TSOs in EU usually do not implement automatic UVLS but leave the decisions to the control room operators. The two options may lead to different impacts in terms of trajectory and final status of the transmission grid under emergency, with different unserved energy. In this paper we analyze the impacts from a technical and economic perspective, modeling the grid behavior with different UVLS schemes (none, manual and automatic). The comparison between the different schemes is done resorting to the Incident Response System (IRS), a software tool developed by the authors in the EU-FP7 SESAME project. An illustrative example to a realistic test case is presented and discussed. This paper shows that automatic UVLS is superior to Manual UVLS, from both technical and economic point of view, due to the fast evolution of voltage collapse phenomena and insufficient time for system operators' manual reaction. The benefits of the scheme involving the automatic UVLS can be then compared with the investment costs of equipping the network with those devices

Dynamic security assessment processing system

The architecture of dynamic security assessment processing system (DSAPS) is proposed to address online dynamic security assessment (DSA) with focus of the dissertation on low-probability, high-consequence events. DSAPS upgrades current online DSA functions and adds new functions to fit into the modern power grid. Trajectory sensitivity analysis is introduced and its applications in power system are reviewed. An index is presented to assess transient voltage dips quantitatively using trajectory sensitivities. Then the framework of anticipatory computing system (ACS) for cascading defense is presented as an important function of DSAPS. ACS addresses various security problems and the uncertainties in cascading outages. Corrective control design is automated to mitigate the system stress in cascading progressions. The corrective controls introduced in the dissertation include corrective security constrained optimal power flow, a two-stage load control for severe under-frequency conditions, and transient stability constrained optimal power flow for cascading outages. With state-of-the-art computing facilities to perform high-speed extended-term time-domain simulation and optimization for large-scale systems, DSAPS/ACS efficiently addresses online DSA for low-probability, high-consequence events, which are not addressed by today\u27s industrial practice. Human interference is reduced in the computationally burdensome analysis