Reliability Studies of Distribution Systems Integrated with Energy Storage

The integration of distributed generations (DGs) - renewable DGs, in particular- into distribution networks is gradually increasing, driven by environmental concerns and technological advancements. However, the intermittency and the variability of these resources adversely affect the optimal operation and reliability of the power distribution system. Energy storage systems (ESSs) are perceived as potential solutions to address system reliability issues and to enhance renewable energy utilization. The reliability contribution of the ESS depends on the ownership of these resources, market structure, and the regulatory framework. This along with the technical characteristics and the component unavailability of ESS significantly affect the reliability value of ESS to an active distribution system. It is, therefore, necessary to develop methodologies to conduct the reliability assessment of ESS integrated modern distribution systems incorporating above-mentioned factors. This thesis presents a novel reliability model of ESS that incorporates different scenarios of ownership, market/regulatory structures, and the ESS technical and failure characteristics. A new methodology to integrate the developed ESS reliability model with the intermittent DGs and the time-dependent loads is also presented. The reliability value of ESS in distribution grid capacity enhancement, effective utilization of renewable energy, mitigations of outages, and managing the financial risk of utilities under quality regulations are quantified. The methodologies introduced in this thesis will be useful to assess the market mechanism, policy and regulatory implications regarding ESS in future distribution system planning and operation. Another important aspect of a modern distribution system is the increased reliability needs of customers, especially with the growing use of sensitive process/equipment. The financial losses of customers due to industrial process disruption or malfunction of these equipment because of short duration (voltage sag and momentary interruption) and long duration (sustained interruption) reliability events could be substantial. It is, therefore, necessary to consider these short duration reliability events in the reliability studies. This thesis introduces a novel approach for the integrated modeling of the short and long duration reliability events caused by the random failures. Furthermore, the active management of distribution systems with ESS, DG, and microgrid has the potential to mitigate different reliability events. Appropriate models are needed to explore their contribution and to assist the utilities and system planners in reliability based system upgrades. New probabilistic models are developed in this thesis to assess the role of ESS together with DG and microgrid in mitigating the adverse impact of different reliability events. The developed methodologies can easily incorporate the complex protection settings, alternate supplies configurations, and the presence of distributed energy resources/microgrids in the context of modern distribution systems. The ongoing changes in modern distribution systems are creating an enormous paradigm shift in infrastructure planning, grid operations, utility business models, and regulatory policies. In this context, the proposed methodologies and the research findings presented in this thesis should be useful to devise the appropriate market mechanisms and regulatory policies and to carry out the system upgrades considering the reliability needs of customers in modern distribution systems

Optimization Techniques for Modern Power Systems Planning, Operation and Control

Recent developments in computing, communication and improvements in optimization techniques have piqued interest in improving the current operational practices and in addressing the challenges of future power grids. This dissertation leverages these new developments for improved quasi-static analysis of power systems for applications in power system planning, operation and control. The premise of much of the work presented in this dissertation centers around development of better mathematical modeling for optimization problems which are then used to solve current and future challenges of power grid. To this end, the models developed in this research work contributes to the area of renewable integration, demand response, power grid resilience and constrained contiguous and non-contiguous partitioning of power networks. The emphasis of this dissertation is on finding solutions to system operator level problems in real-time. For instance, multi-period mixed integer linear programming problem for applications in demand response schemes involving more than million variables are solved to optimality in less than 20 seconds of computation time through tighter formulation. A balanced, constrained, contiguous partitioning scheme capable of partitioning 20,000 bus power system in under one minute is developed for use in time sensitive application area such as controlled islanding

Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model

Battery energy storage systems (BESS) play a pivotal role in future power systems as they contribute to achiev-ing the net-zero carbon emission objectives. The BESS systems, predominantly employing lithium-ion batteries, have been exten-sively deployed. The degradation of these batteries significantly affects system efficiency. Deep neural networks can accurately quantify the battery degradation, however, the model complexity hinders their applications in energy scheduling for various power systems at different scales. To address this issue, this paper pre-sents a novel approach, introducing a linearized sparse neural network-based battery degradation model (SNNBD), specifically tailored to quantify battery degradation based on the scheduled BESS daily operational profiles. By leveraging sparse neural networks, this approach achieves accurate degradation predic-tion while substantially reducing the complexity associated with a dense neural network model. The computational burden of inte-grating battery degradation into day-ahead energy scheduling is thus substantially alleviated. Case studies, conducted on both microgrids and bulk power grids, demonstrated the efficiency and suitability of the proposed SNNBD-integrated scheduling model that can effectively address battery degradation concerns while optimizing day-ahead energy scheduling operations

A Community Microgrid Architecture with an Internal Local Market

This work fits in the context of community microgrids, where members of a community can exchange energy and services among themselves, without going through the usual channels of the public electricity grid. We introduce and analyze a framework to operate a community microgrid, and to share the resulting revenues and costs among its members. A market-oriented pricing of energy exchanges within the community is obtained by implementing an internal local market based on the marginal pricing scheme. The market aims at maximizing the social welfare of the community, thanks to the more efficient allocation of resources, the reduction of the peak power to be paid, and the increased amount of reserve, achieved at an aggregate level. A community microgrid operator, acting as a benevolent planner, redistributes revenues and costs among the members, in such a way that the solution achieved by each member within the community is not worse than the solution it would achieve by acting individually. In this way, each member is incentivized to participate in the community on a voluntary basis. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator. Numerical results obtained on a real test case implemented in Belgium show around 54% cost savings on a yearly scale for the community, as compared to the case when its members act individually.Comment: 16 pages, 15 figure

Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations

This comprehensive review paper explores power system resilience, emphasizing its evolution, comparison with reliability, and conducting a thorough analysis of the definition and characteristics of resilience. The paper presents the resilience frameworks and the application of quantitative power system resilience metrics to assess and quantify resilience. Additionally, it investigates the relevance of complex network theory in the context of power system resilience. An integral part of this review involves examining the incorporation of data-driven techniques in enhancing power system resilience. This includes the role of data-driven methods in enhancing power system resilience and predictive analytics. Further, the paper explores the recent techniques employed for resilience enhancement, which includes planning and operational techniques. Also, a detailed explanation of microgrid (MG) deployment, renewable energy integration, and peer-to-peer (P2P) energy trading in fortifying power systems against disruptions is provided. An analysis of existing research gaps and challenges is discussed for future directions toward improvements in power system resilience. Thus, a comprehensive understanding of power system resilience is provided, which helps in improving the ability of distribution systems to withstand and recover from extreme events and disruptions

Optimization methods for energy management in a microgrid system considering wind uncertainty data

Energy management in the microgrid system is generally formulated as an optimization problem. This paper focuses on the design of a distributed energy management system for the optimal operation of the microgrid using linear and nonlinear optimization methods. Energy management is defined as an optimal scheduling power flow problem. Furthermore, a technical-economic and environmental study is adopted to illustrate the impact of energy exchange between the microgrid and the main grid by applying two management scenarios. Nevertheless, the fluctuating effect of renewable resources especially wind, makes optimal scheduling difficult. To increase the results reliability of the energy management system, a wind forecasting model based on the artificial intelligence of neural networks is proposed. The simulation results showed the reliability of the forecasting model as well as the comparison between the accuracy of optimization methods to choose the most appropriate algorithm that ensures optimal scheduling of the microgrid generators in the two proposed energy management scenarios allowing to prove the interest of the bi-directionality between the microgrid and the main grid.info:eu-repo/semantics/publishedVersio

Optimal Control System of Under Frequency Load Shedding in Microgrid System with Renewable Energy Resources

Book ChapterNowadays many of the power systems are facing serious problems because of the lack of know-how to utilize the available renewable energy resources (RER) so as to balance between the power supply and demand sides. As the consequence of the power unbalancing into their distribution networks, under frequency load shedding (UFLS) which leads to life span reduction of various expensive equipment and deteriorating production in general are of much concerns. Thus, proper control system for the load flow in a system like microgrids (MG) with RER in general is the first thing to carry out the assessment with the aim to solve the power balancing problem within the power system networks. Actually, the major problems which many utilities are facing all over the world are how to utilize the available and future energy resource reserves in order to balance between the supply and demand sides within their power distribution networks. Moreover, because of the quick, improvised and unforeseen increasing number of consumers’ power demands and lack of additional macro energy resources plants which can favorably respond to the instantaneous consumer requirements, optimal control strategy (OCS) is inevitable. The OCS is required to maintain the steady-state operations and ensure the reliability of the entire distribution system over a long period. For that case, the OCS is required to principally stabilize parameters such as voltage, frequency, and limit the injection of reactive power into the MG system under stress. Therefore, in this chapter, the OCS is proposed as an approach to be applied in an intelligent way to solve the UFLS and blackout problems (BP) in a typical MG with RER. The proposed control solution is analyzed using emergency power supply reserves integrated with RER. These typical energy resources can be wind and photovoltaic (solar PV) systems associated with the battery energy storage system (BESS), hydro pump storage, biomass power plant and fuel cell systems