A two-stage stochastic programming model for electric substation flood mitigation prior to an imminent hurricane

We present a stochastic programming model for informing the deployment of temporary flood mitigation measures to protect electrical substations prior to an imminent and uncertain hurricane. The first stage captures the deployment of a fixed number of mitigation resources, and the second stage captures grid operation in response to a contingency. The primary objective is to minimize expected load shed. We develop methods for simulating flooding induced by extreme rainfall and construct two geographically realistic case studies, one based on Tropical Storm Imelda and the other on Hurricane Harvey. Applying our model to those case studies, we investigate the effect of the mitigation budget on the optimal objective value and solutions. Our results highlight the sensitivity of the optimal mitigation to the budget, a consequence of those decisions being discrete. We additionally assess the value of having better mitigation options and the spatial features of the optimal mitigation.Comment: 35 pages, 12 figure

Cyber-Based Contingency Analysis and Insurance Implications of Power Grid

Cybersecurity for power communication infrastructure is a serious subject that has been discussed for a decade since the first North American Electric Reliability Corporation (NERC) critical infrastructure protection (CIP) initiative in 2006. Its credibility on plausibility has been evidenced by attack events in the recent past. Although this is a very high impact, rare probability event, the establishment of quantitative measures would help asset owners in making a series of investment decisions. First, this dissertation tackles attackers\u27 strategies based on the current communication architecture between remote IP-based (unmanned) power substations and energy control centers. Hypothetically, the identification of intrusion paths will lead to the worst-case scenarios that the attackers could do harm to the grid, e.g., how this switching attack may perturb to future cascading outages within a control area when an IP-based substation is compromised. Systematic approaches are proposed in this dissertation on how to systematically determine pivotal substations and how investment can be prioritized to maintain and appropriate a reasonable investment in protecting their existing cyberinfrastructure. More specifically, the second essay of this dissertation focuses on digital protecting relaying, which could have similar detrimental effects on the overall grid\u27s stability. The R-k contingency analyses are proposed to verify with steady-state and dynamic simulations to ensure consistencies of simulation outcome in the proposed modeling in a power system. This is under the assumption that attackers are able to enumerate all electronic devices and computers within a compromised substation network. The essay also assists stakeholders (the defenders) in planning out exhaustively to identify the critical digital relays to be deployed in substations. The systematic methods are the combinatorial evaluation to incorporate the simulated statistics in the proposed metrics that are used based on the physics and simulation studies using existing power system tools. Finally, a risk transfer mechanism of cyber insurance against disruptive switching attacks is studied comprehensively based on the aforementioned two attackers\u27 tactics. The evaluation hypothetically assesses the occurrence of anomalies and how these footprints of attackers can lead to a potential cascading blackout as well as to restore the power back to normal stage. The research proposes a framework of cyber insurance premium calculation based on the ruin probability theory, by modeling potential electronic intrusion and its direct impacts. This preliminary actuarial model can further improve the security of the protective parameters of the critical infrastructure via incentivizing investment in security technologies

Distributed Power Generation Scheduling, Modelling and Expansion Planning

Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. This book deals with distributed energy resources, such as renewable-based distributed generators and energy storage units, among others, considering their operation, scheduling, and planning. Moreover, other interesting aspects such as demand response, electric vehicles, aggregators, and microgrid are also analyzed. All these aspects constitute a new paradigm that is explored in this Special Issue

Intelligent energy management system - techniques and methods

ABSTRACT Our environment is an asset to be managed carefully and is not an expendable resource to be taken for granted. The main original contribution of this thesis is in formulating intelligent techniques and simulating case studies to demonstrate the significance of the present approach for achieving a low carbon economy. Energy boosts crop production, drives industry and increases employment. Wise energy use is the first step to ensuring sustainable energy for present and future generations. Energy services are essential for meeting internationally agreed development goals. Energy management system lies at the heart of all infrastructures from communications, economy, and society’s transportation to the society. This has made the system more complex and more interdependent. The increasing number of disturbances occurring in the system has raised the priority of energy management system infrastructure which has been improved with the aid of technology and investment; suitable methods have been presented to optimize the system in this thesis. Since the current system is facing various problems from increasing disturbances, the system is operating on the limit, aging equipments, load change etc, therefore an improvement is essential to minimize these problems. To enhance the current system and resolve the issues that it is facing, smart grid has been proposed as a solution to resolve power problems and to prevent future failures. This thesis argues that smart grid consists of computational intelligence and smart meters to improve the reliability, stability and security of power. In comparison with the current system, it is more intelligent, reliable, stable and secure, and will reduce the number of blackouts and other failures that occur on the power grid system. Also, the thesis has reported that smart metering is technically feasible to improve energy efficiency. In the thesis, a new technique using wavelet transforms, floating point genetic algorithm and artificial neural network based hybrid model for gaining accurate prediction of short-term load forecast has been developed. Adopting the new model is more accuracy than radial basis function network. Actual data has been used to test the proposed new method and it has been demonstrated that this integrated intelligent technique is very effective for the load forecast. Choosing the appropriate algorithm is important to implement the optimization during the daily task in the power system. The potential for application of swarm intelligence to Optimal Reactive Power Dispatch (ORPD) has been shown in this thesis. After making the comparison of the results derived from swarm intelligence, improved genetic algorithm and a conventional gradient-based optimization method, it was concluded that swam intelligence is better in terms of performance and precision in solving optimal reactive power dispatch problems

Using Renewable-Based Microgrid Capabilities for Power System Restoration

Power system restoration (PSR) is a very important procedure to ensure the consumer supply. In this paper, a decentralized multi-agent system (MAS) for dealing with the microgrid restoration procedure is proposed. In this proposed method, each agent is associated to a consumer or microsource (MS) and these will communicate between each other in order to reach a common decision. The agents solve a 0/1 knapsack problem to determine the best load connection sequence during the microgrid restoration procedure. The proposed MAS is tested in two different case studies: a total blackout and a partial blackout, in which the emergency demand response programs are considered. It is developed in the Matlab/Simulink environment and is validated by performing the corresponding dynamic simulations.Power system restoration (PSR) is a very important procedure to ensure the consumer supply. In this paper, a decentralized multi-agent system (MAS) for dealing with the microgrid restoration procedure is proposed. In this proposed method, each agent is associated to a consumer or microsource (MS) and these will communicate between each other in order to reach a common decision. The agents solve a 0/1 knapsack problem to determine the best load connection sequence during the microgrid restoration procedure. The proposed MAS is tested in two different case studies: a total blackout and a partial blackout, in which the emergency demand response programs are considered. It is developed in the Matlab/Simulink environment and is validated by performing the corresponding dynamic simulations

Network architecture for large-scale distributed virtual environments

Distributed Virtual Environments (DVEs) provide 3D graphical computer generated environments with stereo sound, supporting real-time collaboration between potentially large numbers of users distributed around the world. Early DVEs has been used over local area networks (LANs). Recently with the Internet's development into the most common embedding for DVEs these distributed applications have been moved towards an exploiting IP networks. This has brought the scalability challenges into the DVEs evolution. The network bandwidth resource is the more limited resource of the DVE system and to improve the DVE's scalability it is necessary to manage carefully this resource. To achieve the saving in the network bandwidth the different types of the network traffic that is produced by the DVEs have to be considered. DVE applications demand· exchange of the data that forms different types of traffic such as a computer data type, video and audio, and a 3D data type to keep the consistency of the application's state. The problem is that the meeting of the QoS requirements of both control and continuous media traffic already have been covered by the existing research. But QoS for transfer of the 3D information has not really been considered. The 3D DVE geometry traffic is very bursty in nature and places a high demands on the network for short intervals of time due to the quite large size of the 3D models and the DVE application requirements to transmit a 3D data as quick as possible. The main motivation in carrying out the work presented in this thesis is to find a solution to improve the scalability of the DVE applications by a consideration the QoS requirements of the 3D DVE geometrical data type. In this work we are investigating the possibility to decrease the network bandwidth utilization by the 3D DVE traffic using the level of detail (LOD) concept and the active networking approach. The background work of the thesis surveys the DVE applications and the scalability requirements of the DVE systems. It also discusses the active networks and multiresolution representation and progressive transmission of the 3D data. The new active networking approach to the transmission of the 3D geometry data within the DVE systems is proposed in this thesis. This approach enhances the currently applied peer-to-peer DVE architecture by adding to the peer-to-peer multicast neny_ork layer filtering of the 3D flows an application level filtering on the active intermediate nodes. The active router keeps the application level information about the placements of users. This information is used by active routers to prune more detailed 3D data flows (higher LODs) in the multicast tree arches that are linked to the distance DVE participants. The exploration of possible benefits of exploiting the proposed active approach through the comparison with the non-active approach is carried out using the simulation­based performance modelling approach. Complex interactions between participants in DVE application and a large number of analyzed variables indicate that flexible simulation is more appropriate than mathematical modelling. To build a test bed will not be feasible. Results from the evaluation demonstrate that the proposed active approach shows potential benefits to the improvement of the DVE's scalability but the degree of improvement depends on the users' movement pattern. Therefore, other active networking methods to support the 3D DVE geometry transmission may also be required

Short Term Unit Commitment as a Planning Problem

‘Unit Commitment’, setting online schedules for generating units in a power system to ensure supply meets demand, is integral to the secure, efficient, and economic daily operation of a power system. Conflicting desires for security of supply at minimum cost complicate this. Sustained research has produced methodologies within a guaranteed bound of optimality, given sufficient computing time. Regulatory requirements to reduce emissions in modern power systems have necessitated increased renewable generation, whose output cannot be directly controlled, increasing complex uncertainties. Traditional methods are thus less efficient, generating more costly schedules or requiring impractical increases in solution time. Meta-Heuristic approaches are studied to identify why this large body of work has had little industrial impact despite continued academic interest over many years. A discussion of lessons learned is given, and should be of interest to researchers presenting new Unit Commitment approaches, such as a Planning implementation. Automated Planning is a sub-field of Artificial Intelligence, where a timestamped sequence of predefined actions manipulating a system towards a goal configuration is sought. This differs from previous Unit Commitment formulations found in the literature. There are fewer times when a unit’s online status switches, representing a Planning action, than free variables in a traditional formulation. Efficient reasoning about these actions could reduce solution time, enabling Planning to tackle Unit Commitment problems with high levels of renewable generation. Existing Planning formulations for Unit Commitment have not been found. A successful formulation enumerating open challenges would constitute a good benchmark problem for the field. Thus, two models are presented. The first demonstrates the approach’s strength in temporal reasoning over numeric optimisation. The second balances this but current algorithms cannot handle it. Extensions to an existing algorithm are proposed alongside a discussion of immediate challenges and possible solutions. This is intended to form a base from which a successful methodology can be developed