Improved coordinated automatic voltage control in power grids through complex network analysis

PhD ThesisAutomatic and Co-ordinated Voltage Regulation (CVR) contributes significantly to economy and security of transmission grids. The role of CVR will become more critical as systems are operated closer to their capacity limits due to technical, economic and environmental reasons. CVR has 1 min resolution and owing to the inherent complexity of the task, CVR is enabled through zoning-based Reduced Control Models (RCM) i.e. simplified models of the network suitable for Voltage Control. RCM not only enables CVR bus also affects its performance and robustness. This thesis contributes towards improved CVR through thorough investigation of the RCM. As a starting point, with current power systems structure in mind, this work investigates static RCM schemes (i.e. fixed Reduced Control Model for all network configurations). To that end this thesis develops: (1) a novel generic framework for CVR modelling and evaluation and (2) new zoning-based RCM approaches using Complex Network Analysis. The evaluation of CVR in conjunction with both static and adaptive RCM schemes are based on a novel framework for CVR modelling and evaluation. This framework is generic and can be used to facilitate the selection and design of any of the CVR components. As a next step, due to the fact that a single RCM cannot be optimal for all network configurations, adaptive RCM (i.e. RCM determined in an online event driven fashion) is investigated using the proposed framework. This concerns future transmission grids where RCM is driven by the need for reliability rather than economy of measurement points at a planning phase. Lastly, this thesis examines zone division in an interconnected system ranging from EHV down to MV, and assesses the required degree of co-ordination for the voltage control of these zones. Essentially, this last item extends the scope of this work’s contributions beyond a single transmission-level Independent System Operator (ISO).EPSRC for funding my Research and the Consortium of the “Autonomic Power System” project

Self-organising multi-agent control for distribution networks with distributed energy resources

Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time.Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time

Immune System Based Control and Intelligent Agent Design for Power System Applications

The National Academy of Engineering has selected the US Electric Power Grid as the supreme engineering achievement of the 20th century. Yet, this same grid is struggling to keep up with the increasing demand for electricity, its quality and cost. A growing recognition of the need to modernize the grid to meet future challenges has found articulation in the vision of a Smart Grid in using new control strategies that are intelligent, distributed, and adaptive. The objective of this work is to develop smart control systems inspired from the biological Human Immune System to better manage the power grid at the both generation and distribution levels. The work is divided into three main sections. In the first section, we addressed the problem of Automatic Generation Control design. The Clonal Selection theory is successfully applied as an optimization technique to obtain decentralized control gains that minimize a performance index based on Area Control Errors. Then the Immune Network theory is used to design adaptive controllers in order to diminish the excess maneuvering of the units and help the control areas comply with the North American Electric Reliability Corporation\u27s standards set to insure good quality of service and equitable mutual assistance by the interconnected energy balancing areas. The second section of this work addresses the design and deployment of Multi Agent Systems on both terrestrial and shipboard power systems self-healing using a novel approach based on the Immune Multi-Agent System (IMAS). The Immune System is viewed as a highly organized and distributed Multi-Cell System that strives to heal the body by working together and communicating to get rid of the pathogens. In this work both simulation and hardware design and deployment of the MAS are addressed. The third section of this work consists in developing a small scale smart circuit by modifying and upgrading the existing Analog Power Simulator to demonstrate the effectiveness of the developed technologies. We showed how to develop smart Agents hardware along with a wireless communication platform and the electronic switches. After putting together the different designed pieces, the resulting Multi Agent System is integrated into the Power Simulator Hardware. The multi Agent System developed is tested for fault isolation, reconfiguration, and restoration problems by simulating a permanent three phase fault on one of the feeder lines. The experimental results show that the Multi Agent System hardware developed performed effectively and in a timely manner which confirms that this technology is very promising and a very good candidate for Smart Grid control applications

Economic zones for future complex power systems

This paper examines the economics of the electricity market out to 2050. We propose a flexible zoning concept, built up around economic and technical layers, in networks of the order of hundreds of thousands or millions of nodes. The Economic Layer runs auctions to determine the electricity to be delivered and prices. The Economic Layer delivers suggestions after a fixed ordering, starting with suppliers and demands that generates the lowest overall system cost, then second-lowest overall network cost etc. These suggestions are delivered to the Technical Layer that checks for feasibility in terms of technical constraints. The first match between the ranked suggestions and non-violation of technical constraints is chosen. We demonstrate why this paper should be considered for future power systems. This paper extends previous work on reactive power exchange by introducing market considerations in zoning mechanisms for active power exchanges. We are also exhibit the potential for much higher price resolution in distribution networks via our concept of economic zoning

Decentralised Optimisation and Control in Electrical Power Systems

Emerging smart-grid-enabling technologies will allow an unprecedented degree of observability and control at all levels in a power system. Combined with flexible demand devices (e.g. electric vehicles or various household appliances), increased distributed generation, and the potential development of small scale distributed storage, they could allow procuring energy at minimum cost and environmental impact. That however presupposes real-time coordination of demand of individual households and industries down at the distribution level, with generation and renewables at the transmission level. In turn this implies the need to solve energy management problems of a much larger scale compared to the one we currently solve today. This of course raises significant computational and communications challenges. The need for an answer to these problems is reflected in today’s power systems literature where a significant number of papers cover subjects such as generation and/or demand management at both transmission and/or distribution, electric vehicle charging, voltage control devices setting, etc. The methods used are centralized or decentralized, handling continuous and/or discrete controls, approximate or exact, and incorporate a wide range of problem formulations. All these papers tackle aspects of the same problem, i.e. the close to real-time determination of operating set-points for all controllable devices available in a power system. Yet, a consensus regarding the associated formulation and time-scale of application has not been reached. Of course, given the large scale of the problem, decentralization is unavoidably part of the solution. In this work we explore the existing and developing trends in energy management and place them into perspective through a complete framework that allows optimizing energy usage at all levels in a power system

Hydrogen and Peer-to-Peer Energy Exchanges for Deep Decarbonization of Power Systems

Decreasing costs of renewable energy resources and net-zero emission energy production policy, set by U.S. government, are two preeminent factors that motivate power utilities to deploy more system- or consumer-centric distributed energy resources (DERs) to decarbonize electricity production. Since, deep energy decarbonization cannot be achieved without high penetration of renewable energy sources, utilities should develop and invest in new business models for power system operation and planning during the energy transition. Considering the pathways to deeply decarbonize power systems, first, this dissertation proposes a novel hierarchical peer-to-peer (P2P) energy market design in active distribution networks. The framework integrates the distributional locational marginal price to a multi-round double auction with average price mechanism to integrate the network usage charges into the bills of customers. Second, this dissertation investigates the role of grid-integrated hydrogen (H2) systems for improved utility operations and to supply fuel to transportation sector. Power quality concerns as well as risk of uncertain parameters are considered using conditional value at risk based epsilon constraint method. Third, this dissertation proposes a bi-level proactive rolling-horizon based scheduling of H2 systems in integrated distribution and transmission networks considering the flexibility of these assets as controllable load or generation, in addressing the utility operators\u27 normal and emergency operation signals. Fourth, a detailed model is developed for grid-integrated Electrolyzer considering polarization curve and non-linear conversion efficiency of these assets in the P2P enabled distribution network. This framework shows that reasonable penetration of P2P energy exchanges can significantly lower the H2 production cost. Finally, this dissertation proposes a cyber-physical vulnerability assessment of P2P energy exchanges in an unbalanced active distribution networks. Simulation results of this dissertation show the effectiveness of the proposed frameworks