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Master's Thesis from the year 2019 in the subject Energy Sciences, grade: 1.0, Technical University of Munich, language: English, abstract: With the rising adoption of Electric Vehicle (EV) technology and Renewable Energy Sources (RES), electric distribution grids are facing new challenges regarding congestion management. The present work steps into the topic of controlled charging mechanisms to reduce physical grid extension by utilizing flexible loads from EV. Although, existing research concludes a positive impact on congestion relief, less attention is given to a holistic and light system that is implementable under current circumstances. This thesis develops a novel system towards micro-auctions for local flexibility allocation amongst EVs to reduce grid congestion. A functional software prototype simulates a virtual market and grid environment. Each EV acts as an autonomous agent submitting bids to the local flexibility market, offering 15-minute charging breaks. Based on individual risk preference and state-of-charge, bidprices vary amongst EVs. The Distribution Grid Operator (DSO) constantly asses grid status and contracts positive capacity during critical phases by accepting current bids. It can be shown, that regardless of the penetration rate of EVs, the proposed model balances the tested grid topology below the maximum workload and within a predefined range. According to simulation assumptions, a ninefold increase of EVs can be accommodated with the proposed model. Although, with monotonically increasing penetration rate, average charge-increase converges to zero. Due to the proposed intervals, EVs are grouped to continues batches with demandresponse latency. Once contracted, EVs remain charging or not-charging for 15 minutes. The assignment to certain batches does not change over simulation time. Based on the proposed request control mechanism, critical grid conditions can be reduced by 49%. Whereas quantitative results are limited to the proposed simulation assumptions, qualitative effects are generalizable to a certain extend.
High penetration of renewable energy sources (RESs) imposes several techno-economic challenges to distribution system operators (DSOs) due to their variability in power generation and hence, increases the need for additional operational flexibility. Operational flexibility aims at securely covering the possible variations at least cost using emerging flexible alternatives or designing novel local market mechanisms to incentivize flexibility providers. In such a situation, the DSOs can use the potential of flexible options such as Energy Storages (ESs), Demand Response (DR), Plug-in Electric Vehicles (PEVs) or on-site fast run generators. However, each of the mentioned flexible resources has its own specific characteristics and requirements that should be taken into account and this raises the problem complexity. Optimal network reconfiguration schemes are also the other solution for increasing power system flexibility at distribution level. There is a great research gap related to renewable-based distribution network planning with flexibility point of view. Therefore, this book aims to discuss the additional flexibility needs introduced by RESs and describe general approaches to analyze the need for and provision of additional flexibility in future distribution networks at both planning and operational time frames. This book successfully suggests new solutions and techniques to increase the flexibility in distribution systems. This book also highlights the needs for moving towards smart distribution grids in order to enhance the flexibility in modern and future power systems.
The feasible operation region (FOR) allows capturing the aggregated flexibility potential of DER within radial distribution networks, while respecting the technical restrictions of both plants and grid. This thesis proposes a novel approach to compute the FOR, the Linear Flexibility Aggregation (LFA) method, based on the solution of a sequence of linear OPF. With the objective of reducing the computation time, without compromising the accuracy of the assessed FOR. It is shown that the proposed method provides a considerable reduction in processing time compared to similar methods, e.g. Monte-Carlo simulations or non-linear OPF-based methods.
ACTIVE ELECTRICAL DISTRIBUTION NETWORK Discover the major issues, solutions, techniques, and applications of active electrical distribution networks with this edited resource Active Electrical Distribution Network: A Smart Approach delivers a comprehensive and insightful guide dedicated to addressing the major issues affecting an often-overlooked sector of the electrical industry: electrical distribution. The book discusses in detail a variety of challenges facing the smart electrical distribution network and presents a detailed framework to address these challenges with renewable energy integration. The book offers readers fulsome analyses of active distribution networks for smart grids, as well as active control approached for distributed generation, electric vehicle technology, smart metering systems, smart monitoring devices, smart management systems, and various storage systems. It provides a treatment of the analysis, modeling, and implementation of active electrical distribution systems and an exploration of the ways professionals and researchers from academia and industry attempt to meet the significant challenges facing them. From smart home energy management systems to approaches for the reconfiguration of active distribution networks with renewable energy integration, readers will also enjoy: A thorough introduction to electrical distribution networks, including conventional and smart networks An exploration of various existing issues related to the electrical distribution network An examination of the importance of harmonics mitigation in smart distribution networks, including active filters A treatment of reactive power compensation under smart distribution networks, including techniques like capacitor banks and smart devices An analysis of smart distribution network reliability assessment and enhancement Perfect for professionals, scientists, technologists, developers, designers, and researchers in smart grid technologies, security, and information technology, Active Electrical Distribution Network: A Smart Approach will also earn a place in the libraries of policy and administration professionals, as well as those involved with electric utilities, electric policy development, and regulating authorities.
In this paper optimal grid design problems are revisited in view of the ongoing transformations in distribution systems. The transformations are those caused by distributed generation, changes in load use, and smart grid operation. These transformations have an expressive impact on the way planning must be carried out. Trends on grid design are advanced to deal effectively with future problems of security of supply in the context of advanced grid operation and demand responsive resources as enabled by grid modernization technologies. Formulations of key optimization problems in grid design are provided together with the required modelling of load behavior. Solution challenges for the key problems are identified and the corresponding stochastic framework for chronological simulation is advanced as favored by a plethora of newly available load-data. Required developments in decision support tools for planning the distribution grid of the future are finally discussed.
Among the various major components of the electric power system, distribution grids have not received a lot of attention from the research community. Today's distribution grids are experiencing a significant transformation due to an increasing amount of smart electric loads and distributed energy resources (DERs), such as electric vehicles, rooftop solar photovoltaic (PV) systems and energy storage. Growing penetration of DERs increase the power injection variability in distribution grids, leading to power quality issues such as voltage unbalance and voltage variations. The intermittent nature of these DERs and load forecasting errors can also increase uncertainty in distribution grids. Furthermore, DERs are managed by different entities such as distribution utility or third-party aggregators whose objectives are conflicting with each other. This makes it challenging to precisely control DER behavior to optimize grid operations. This work introduces an optimization-based approach to improve power quality in unbalanced distribution grids and effectively exploit the flexibility offered by DERs. DERs such as solar PV systems with power electronic inverters can be employed as a cost-effective solution to provide reactive power support and minimize voltage violations and unbalance. We first develop a three-phase optimal power flow (OPF) formulation to identify DER control actions that improve power quality. A major challenge to efficiently solving the three-phase OPF is computation time. To reduce computational complexity, three linear power flow models which are well suited to analyze voltage unbalance are utilized as part of a successive approximation approach to provide good quality solutions that are AC feasible. The OPF formulation is then extended to examine the impact of uncertainty on distribution grid operations by leveraging a chance-constrained optimization approach. By using this method, we identify a set of DER control actions that can be utilized across a range of highly uncertain scenarios. Finally, the challenges of harnessing the flexibility offered by DERs, which are controlled by different entities in the grid, is addressed. A bilevel optimization approach is proposed to determine the collective DER and load flexibility in the network. Our case studies demonstrate that the resulting aggregate power flexibility range can help reduce burdens on transmission system resources while maintaining acceptable power quality in distribution grids.
Increasing numbers in distributed generation units and growing electrification of the transportation sector leads to new challenges in electrical distribution grids. In this thesis, a holistic approach, including different functional layers for the operation, monitoring, and control of low voltage grids, is presented. Power flow methods are usually used to monitor the grid, but for control purposes, their implicit and non-linear character is quite challenging. This work introduces a linear explicit power flow approximation. It exploits on-line information combined with pseudo measurements to adapt to operating points of the grid. The lack of this functionality is a primary source of error in standard off-line methods. Needed grid parameters for the approximation, are calculated with an approach that combines a dynamic thermal model of the power cables with a mean value estimation of the impedance. Thus, resistive parameter changes due to load currents can be tracked during grid operation.The first operational layer is designed as a distributed model predictive control (DMPC). Its purpose is to better unify three-phase generation units, charging facilities, and dominant consumers in low voltage grids. It maximizes the transport capacity of the network, keeps sensitive data from each controller private, and considers the limitation of grid assets. A secondary layer deals with the inherent unbalance in low voltage grids. The approach uses a Jacobi based distributed optimization algorithm to coordinate local, flexible electric power. With the developed power flow approximation, it is possible to formulate a local optimization problem, that does not scale with grid size. Additionally, it can directly reduce the negative- and zero-sequence components without the need for additional measurements.
Discusses flexibility issues in modern and future Smart power systems. Discusses flexible smart distribution grid with renewable-based distributed generation. Explains high penetration level of renewable energy sources and flexibility issues. Highlights flexibility based on energy storages, demand response, and plug-in electric vehicles. Describes Flexibility sources in modern power systems.
This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data- driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flow equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.