Modeling and Analysis of Remote, Off-grid Microgrids

Over the past century the electric power industry has evolved to support the delivery of power over long distances with highly interconnected transmission systems. Despite this evolution, some remote communities are not connected to these systems. These communities rely on small, disconnected distribution systems, i.e., microgrids, to deliver power. Power distribution in most of these remote communities often depend on a type of microgrid called off-grid microgrids\u27\u27. However, as microgrids often are not held to the same reliability standards as transmission grids, remote communities can be at risk to experience extended blackouts. Recent trends have also shown an increased use of renewable energy resources in power systems for remote communities. The increased penetration of renewable resources in power generation will require complex decision making when designing a resilient power system. This is mainly due to the stochastic nature of renewable resources that can lead to loss of load or line overload during their operations. In the first part of this thesis, we develop an optimization model and accompanying solution algorithm for capacity planning and operating microgrids that include N-1 security and other practical modeling features (e.g., AC power flow physics, component efficiencies and thermal limits). We demonstrate the effectiveness of our model and solution approach on two test systems: a modified version of the IEEE 13 node test feeder and a model of a distribution system in a remote Alaskan community. Once a tractable algorithm was identified to solve the above problem, we develop a mathematical model that includes topology design of microgrids. The topology design includes building new lines, making redundant lines, and analyzing N-1 contingencies on generators and lines. We develop a rolling horizon algorithm to efficiently analyze the model and demonstrate the strength of our algorithm in the same network. Finally, we develop a stochastic model that considers generation uncertainties along with N-1 security on generation assets. We develop a chance-constrained model to analyze the efficacy of the problem under consideration and present a case study on an adapted IEEE-13 node network. A successful implementation of this research could help remote communities around the world to enhance their quality of life by providing them with cost-effective, reliable electricity

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

Hosting Capacity Optimization in Modern Distribution Grids

The availability of distributed renewable energy resources and the anticipated increase in new types of loads are changing the way electricity is being produced and supplied to consumers. This shift is moving away from a network delivering power solely from centralized power plants towards a decentralized network which supplements its power production by incorporating local distributed generators (DGs). However, the increased integration of DGs into existing distribution networks is impacting their behavior in terms of voltage profile, reliability, and power quality. To determine the maximum amount of DG that distribution grids can accommodate the concept of hosting capacity is introduced. The distribution grid hosting capacity is defined as the amount of new production or consumption that can be added to the grid without adversely impacting the reliability or voltage quality for other customers. The study of the hosting capacity is commonly accomplished by simulating power flow for each potential placement of DG while enforcing operating limits (e.g. voltage limits and line thermal limits). Traditionally, power flow is simulated by solving full nonlinear AC power flow equations for each potential configuration. Existing methods for computing hosting capacity require extensive iterations, which can be computationally-expensive and lack solution optimality. In this dissertation, several approaches for determining the optimal hosting capacity are introduced. First, an optimization-based method for determining the hosting capacity in distribution grids is proposed. The method is developed based on a set of linear power flow equations that enable linear programming formulation of the hosting capacity model. The optimization-based hosting capacity method is then extended to investigate further increasing hosting capacity by also optimizing network reconfiguration. The network reconfigurations use existing switches in the system to increase allowable hosting capacity without upgrading the network infrastructure. Finally, a sensitivity-based method is described which more efficiently obtains the optimal hosting capacity for larger distribution systems. The proposed methods are examined on several test radial distribution grids to show their effectiveness and acceptable performance. Performance is further measured against existing iterative hosting capacity calculation methods. Results demonstrate that the proposed method outperforms traditional methods in terms of computation time while offering comparable results

Optimal Operation and Maximal Hosting Capacity of High-Renewable Islanded Microgrids

With the advancement of technology, renewable power generators such as solar photovoltaics and wind turbines have become cost-effective and competitive compared to traditional generators. On the other hand, carbon emission issues have been globally focused, promoting development of renewable energy. Meanwhile, microgrids have been widely constructed with increasing installation of distributed generators including microturbines and renewable power generators. Challenges from intermittent and uncertain renewable sources, low operating efficiency as well as system stability in the islanded mode still exist for microgrid operation and renewable hosting capacity assessment. To address these unsolved issues, it is worth developing advanced optimal operation and hosting capacity maximization approaches for high-renewable microgrids, which are presented in this thesis. For microgrid operation, economic efficiency, solution robustness and system stability are major concerns to be addressed. In order to achieve cost-effective operation, firstly a new stochastic optimal power flow (OPF) is proposed for islanded microgrids. A linear network operating model which can be used in the OPF problem is specifically developed, while uncertainties of photovoltaic power and loads are addressed by Monte Carlo simulation. Secondly, an improved OPF method with a new iterative solution algorithm is proposed to enhance the accuracy of network operating model and the computing speed. Besides, an advanced probabilistic modelling method is adapted to present real-time uncertainties in the OPF method. Thirdly, a novel stochastic OPF method with consideration of tie-line switching from the grid-connected to the islanded mode while the main grid in contingency is proposed. Security constraints to guarantee the system stability in the islanded mode are formulated. Moreover, a Benders decomposition based solution algorithm is developed, to efficiently solve the OPF problem with a master problem and a sub-problem which formulate the grid-connected and the islanded modes, respectively. Fourthly, a renewable hosting capacity maximization approach for an islanded microgrid, considering system frequency deviation, is proposed. An advanced sensitivity region based optimization method is proposed to address the uncertainties of wind power and loads, thus obtaining a robust solution. The proposed methods have been successfully demonstrated and compared with existing works. Simulation results have verified their feasibility and effectiveness

Local Market Mechanisms: how Local Markets can shape the Energy Transition

Europe has embarked on a journey towards a zero-emission system, with the power system at its core. From electricity generation to electric vehicles, the European power system must transform into an interconnected, intelligent network. To achieve this vision, active user participation is crucial, ensuring transparency, efficiency, and inclusivity. Thus, Europe has increasingly focused on the concept of markets in all their facets. This thesis seeks to answer the following questions: How can markets, often considered abstract and accessible only to high-level users, be integrated for end-users? How can market mechanisms be leveraged across various phases of the electrical system? Why is a market- driven approach essential for solving network congestions and even influencing planning? These questions shape the core of this research. The analysis unfolds in three layers, each aligned with milestones leading to 2050. The first explores how market mechanisms can be integrated into system operator development plans, enhancing system resilience in the face of changes. In this regard, this step addresses the question of how a market can be integrated into the development plans of a network and how network planning can account for uncertainties. Finally, the analysis highlights the importance of sector coupling in network planning, proposing a study in which various energy vectors lead to a multi-energy system. According to the roadmap to 2030, this layer demonstrates how markets can manage several components of the gas and electrical network. Finally, even though the robust optimisation increases the final cost in the market, it allows to cover the system operator from uncertainties. The second step delves into the concept of network congestion. While congestion management is primarily the domain of operators, it explores how technical and economic collaboration between operators and system users, via flexibility markets, can enhance resilience amid demand uncertainties and aggressive market behaviours. In addition to flexibility markets, other congestion markets are proposed, some radically different, like locational marginal pricing, and others more innovative, such as redispatching markets for distribution. Building upon the first analysis, this section addresses questions of how various energy vectors can be used not only to meet demand but also to manage the uncertainties associated with each resource. Consequently, this second part revisits the concept of sector coupling, demonstrating how various energy vectors can be managed through flexibility markets to resolve network congestion while simultaneously handling uncertainties related to different vectors. The results demonstrate the usefulness of the flexibility market in managing the sector coupling and the uncertainties related to several energy vectors. The third and most innovative step proposes energy and service markets for low-voltage users, employing distributed ledger technology. Since this step highlights topics that are currently too innovative to be realized, this third section offers a comparative study between centralised and decentralised markets using blockchain technology, highlighting which aspects of distributed ledger technology deserve attention and which aspects of low-voltage markets need revision. The results show that the blockchain technology is still in the early stage of its evolution, and several improvements are needed to fully apply this technology into real-world applications. To sum up, this thesis explores the evolving role of markets in the energy transition. Its insights are aimed at assisting system operators and network planners in effectively integrating market mechanisms at all levels of

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas