A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

A Comprehensive Review of Congestion Management in Power System

In recent decades, restructuring has cut across all probable domains, involving the power supply industry. The restructuring has brought about considerable changes whereby electricity is now a commodity and has become a deregulated one. These competitive markets have paved the way for countless entrants. This has caused overload and congestion on transmission lines. In addition, the open access transmission network has created a more intensified congestion issue. Therefore, congestion management on power systems is relevant and central significance to the power industry. This manuscript review few congestion management techniques, consists of Reprogramming Generation (GR), Load Shedding, Optimal Distributed Generation (DG) Location, Nodal Pricing, Free Methods, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic System Method, as well as Additional Renewable Energy Sources. In this manuscript a review work is performed to unite the entire publications on congestion management

Congestion Management by Applying Co-operative FACTS and DR program to Maximize Renewables

This research proposes an incremental welfare consensus method based on flexible alternating current transmission systems (FACTS) and demand response (DR) programs to control transmission network congestion in order to increase the penetration of wind power. The locational marginal prices are used as input by the suggested model to control the FACTS device and DR resources. In order to do this, a cutting-edge two-stage market clearing system is created. In the first stage, participants bid on the market with the intention of maximizing their profits, and the ISO clears the market with the goal of promoting societal welfare. The second step involves the execution of a generation re-dispatch issue in which incentive-based DR and FACTS device controllers are optimally coordinated to reduce the rescheduling expenses for generating firms. Here, a static synchronous compensator and a series capacitor operated by a thyristor are used as two different forms of FACTS devices. A case study on the modified IEEE one-area 24-bus RTS system is then completed. The simulation results show that the suggested interactive DR and FACTS model not only reduces system congestion but also makes the system more flexible so that it can capture as much wind energy as feasible.Comment: 23 pages, 8 figures, 8 table

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities

Optimal and Secure Electricity Market Framework for Market Operation of Multi-Microgrid Systems

Traditional power systems were typically based on bulk energy services by large utility companies. However, microgrids and distributed generations have changed the structure of modern power systems as well as electricity markets. Therefore, restructured electricity markets are needed to address energy transactions in modern power systems. In this dissertation, we developed a hierarchical and decentralized electricity market framework for multi-microgrid systems, which clears energy transactions through three market levels; Day-Ahead-Market (DAM), Hour-Ahead-Market (HAM) and Real-Time-Market (RTM). In this market, energy trades are possible between all participants within the microgrids as well as inter-microgrids transactions. In this approach, we developed a game-theoretic-based double auction mechanism for energy transactions in the DAM, while HAM and RTM are cleared by an optimization algorithm and reverse action mechanism, respectively. For data exchange among market players, we developed a secure data-centric communication approach using the Data Distribution Service. Results demonstrated that this electricity market could significantly reduce the energy price and dependency of the multi-microgrid area on the external grid. Furthermore, we developed and verified a hierarchical blockchain-based energy transaction framework for a multi-microgrid system. This framework has a unique structure, which makes it possible to check the feasibility of energy transactions from the power system point of view by evaluating transmission system constraints. The blockchain ledger summarization, microgrid equivalent model development, and market players’ security and privacy enhancement are new approaches to this framework. The research in this dissertation also addresses some ancillary services in power markets such as an optimal power routing in unbalanced microgrids, where we developed a multi-objective optimization model and verified its ability to minimize the power imbalance factor, active power losses and voltage deviation in an unbalanced microgrid. Moreover, we developed an adaptive real-time congestion management algorithm to mitigate congestions in transmission systems using dynamic thermal ratings of transmission lines. Results indicated that the developed algorithm is cost-effective, fast, and reliable for real-time congestion management cases. Finally, we completed research about the communication framework and security algorithm for IEC 61850 Routable GOOSE messages and developed an advanced protection scheme as its application in modern power systems

Calculating the profits of an economic MPC applied to CSP plants with thermal storage system

Electricity producers participating in a day-ahead energy market aim to maximize profits derived from electricity sales. The daily generation schedule has to be offered in advance, usually the previous day before a certain moment in time. The development of an economically-optimal generation schedule is the core of the generation scheduling problem. To solve this problem, renewable energy plant owners need, besides energy prices forecast, weather prediction. Among renewable energy sources, concentrated solar power (CSP) plants with thermal energy storage (TES) may find it easier to participate in electricity markets due to their semi-dispatchable generation. In any case, the limited accuracy of forecasting solar resource brings about the risk of penalties that may be imposed to CSP plants for deviation from the submitted schedule. This paper proposes a model-based predictive control (MPC) approach with an economic objective function to tackle the scheduling problem in CSP plants with TES. By this approach, the most recent forecast and the current status of plant can be used by the proposed economic MPC approach to reschedule the generation conveniently at regular time intervals. On the other hand, a more feasible generation schedule for the next day is performed at the appropriate time thanks to the use of short-term forecast. The proposed approach is applied, in a simulation context, to a 50 MW parabolic trough collector-based CSP plant with TES under the assumptions of perfect price forecasts and participation in the Spanish day-ahead energy market. A case study based on a half-year period to test several meteorological conditions is performed. In this study, an economic analysis is carried out using actual values of energy price, penalty cost, solar resource data and its day-ahead forecast. Results show an economic improvement in comparison with a traditional day-ahead scheduling strategy, especially in periods with a bad weather forecast. To overcome the lack of short-term weather forecast data for this study, a synthetic short-term predictor, whose accuracy level can be tuned by means of a parameter, is used. Sweeping this accuracy level between the situation with no forecast improvement and perfect shortterm forecast, the MPC strategy reaches an improvement in total profits during the six months period between 13.9% and 33.3% of the maximum room for improvement. This maximum ideal improvement is defined as the difference in profits between the MPC strategy with perfect forecasts and the dayahead scheduling strategy.This research has been supported by DPI2016-76493-C3-2-R Project of Ministerio de Economía y Competitividad (Spain). The authors would like to thank Acciona Energa S.A. for expressing interest in the projec

Congestion management optimization in electric transmission system

Congestion management in electric transmissionsystems is one of the most important challenges for powersystems with high penetration of renewable energy. Systemcongestion occurs when the desired power flow cannot betransmitted through the network without violating systemoperating limits. In order to prevent severe system damage, asignificant number of congestion management methods havebeen developed, including nodal pricing, load shedding,curtailment of renewable energy generation, generatorrescheduling, optimal transmission switching, etc. Most of thesemethods, however, do not comply with the optimal operation ofconventional power plants subjected to dynamic constraints(manoeuvrability, start-up and shut down times, etc.). In thispaper, the rescheduling generation (or re-dispatch optimization)problem is solved using a modified particle swarm optimization(PSO) algorithm which accounts for start up as well as shutdown times, and the manoeuvrability of conventional powerplants

Power System Stability Assessment and Enhancement using Computational Intelligence

The main objective of the dissertation is to develop a fast and robust tool for assessment of power system stability and design a framework for enhancing system stability. The proposed framework is - based on the investigation of the dynamic behavior of the system - a market based rescheduling strategy that increases the stability margin. The dissertation specifically puts emphasis on the following approached: Power System Stability Evaluation: System stability is investigated by simulating a set of critical contingencies to determine whether the disturbances will result in any unsafe operating conditions and extract the necessary information to classify system states. The classification is based on the computation of the critical fault clearing time (CCT) for transient stability assessment (TSA) and the minimum damping of oscillation (MDO) for power system oscillatory stability assessment (OSA). The customary method of power system transient stability analysis including time-domain simulation (TDS) is used to compute the CCT at each critical contingency and Prony analysis as an efficient identification technique to estimate the mode parameters from the actual time response. The use of Prony analysis is to account for the effects of the change in location of the small disturbances as well as the increase in system nonlinearity on oscillating modes. Fast Power System Stability Assessment Tool: An artificial neural network (ANN) is designed to serve as accurate and fast tool for dynamic stability assessment (DSA). Fast response of ANN allows system operators to take suitable control actions to enhance the system stability and to forestall any possible impending breakup of the system. Two offline trained ANN are designed to map the dynamic behavior by relating the selected input features and the calculated CCT (as indicator for transient stability) and MDO (as indicator for oscillatory stability). Input features of ANN are selected to characterize the following: Changes in system topology and power distributions due to outage of major equipment such as transmission line, generation unit or large load Change in fault location and the severity of the fault Variation in loading levels and load allocation among market participants The features are generated for a wide range of loading at each expected system topology. Initial feature sets are pre-selected by engineering judgment based on experience in power system operation. In order to improve the accuracy of ANN to map the power system dynamic behavior, final selection is performed in the following three steps. In the first step, the generators terminal voltage drops immediately after fault are selected features to characterize the severity of the contingency with respect to the generators and to detect the fault location. In the second step, new features based on the inertia constant and the generated power in each area are calculated to characterize the changes in system topology and power flow pattern during normal and abnormal operation. In the third step, a systematic clustering feature selection technique is used to select the most important features that characterize the load levels and the power flow through lines from the mathematical viewpoint. The results prove the suitability of ANN in DSA with a reasonable degree of accuracy. Dynamic Stability Enhancement: To achieve online dynamic stability enhancement an online market based rescheduling strategy is proposed in the deregulated power systems. In case of power system operation by a centralized pool in vertically integrated electric utilities, generation rescheduling based sensitivity analysis is proposed. In the proposed market for deregulated power systems, the transactions among suppliers and consumers participating in the market are reallocated based on optional power bids to enhance system stability in case the available control actions are insufficient to enhance system stability. All participants are allowed to submit voluntary power bids to increase or decrease their scheduled level with equal chance. These bids represent the offered power quantity and the corresponding price. The goal of the framework is to enhance system stability with minimum additional and opportunity costs arising from the rescheduling. In case of vertically integrated electric utility, generation rescheduling based sensitivity analysis is used to enhance the system stability. The sensitivity analysis is based on the generators response following the most probable contingency. The generators are split into critical machines with positive sensitivity and non-critical machines with negative sensitivity. The change of the generation level among critical and non-critical machines provides the trajectories for stabilization procedure. The re-allocation of power among generators in each group is calculated based on the generator capacities and inertia constant, which simplifies the optimization procedure and speeds up the iterative to find a feasible solution. The objective is to minimize the increase in the cost due to rescheduling process. Particle swarm optimization is used as an optimization tool to search for the optimal solution to enhance the system stability with a minimum cost. The handling of all system constraints including stability constraints is achieved using a self-adaptive penalty function. Comparison strategy for selecting the best individuals during the optimization process is proposed where the feasible solutions are ever preferable during selection of local and global best particles.Die Schwerpunkte der Dissertation liegen in der Entwicklung eines schnellen und robusten Echtzeit-Bewertungsinstruments für Stabilitätsuntersuchungen in elektrischen Energienetzen und in dem Entwurf von Rahmenbedingungen zur Verbesserung der Systemstabilität. Basierend auf Untersuchungen bezüglich des dynamischen Verhaltens von elektrischen Energienetzen ist das Ziel der vorgeschlagenen Rahmenbedingungen, eine Planungsstrategie zu entwickeln, die marktwirtschaftlich ausgerichtet ist, um so die Stabilitätsgrenze zu verbessern und die erforderliche Systemsicherheit zu gewährleisten. Die dynamische Stabilität von elektrischen Energienetzen wurde bezogen auf die transiente und oszillatorische Stabilität untersucht, welche zur Beurteilung des dynamischen Verhaltens des Systems während Netzstörungen genutzt wird. Das Ziel der Dissertation ist die folgenden Aspekte zu untersuchen: Evaluierung der Dynamischen Stabilität: Die dynamische Stabilität ist durch die Simulation von kritischen Netzereignissen untersucht worden. Ziel war es, Störungen zu ermitteln, die zu kritischen oder gar unsicheren Betriebszuständen führen, und wichtige Beurteilungsparameter über den Zustand des Netzes auszuwählen. Die Beurteilungsparameter über den Zustand des elektrischen Energienetzes sind unter Verwendung der kritischen Fehlerklärungszeit als Indikator für die transiente Stabilität und der minimalen Dämpfung von Oszillationen als Indikator für die ozillatorische Stabilität ermittelt worden. Die übliche Methode bei einer transienten Stabilitätsanalyse in elektrischen Energienetzen basiert auf Simulationen im Zeitbereich und wird unter der Verwendung von vordefinierten netzkritischen Ereignissen genutzt, um die kritische Fehlerklärungszeit präzise zu berechnen. Die Prony-Analyse als eine effiziente Identifizierungstechnik wird zur Schätzung der Zustandsparameter auf eine einer Störung folgenden Zeitantwort verwendet. Der Gebrauch der Prony-Analyse erfasst die Veränderungen im Fehlerort von kleinen Störungen und einen Anstieg von Systemnichtlinearitäten im oszillatorischen Modus. Die mit Hilfe der Modalanalyse berechneten Parameter für den oszillatorischen Modus werden als Referenzsignale während des Abstimmens der Parameter der Prony-Analyse verwendet. Ziel ist die Verbesserung der Identifizierung des Systemmodus. Schnelles Bewertungswerkzeug für die dynamische Stabilität: Ein präzises und schnelles Werkzeug für die Bewertung von dynamischer Stabilität wurde mit Hilfe von künstlichen, neuronalen Netzen entwickelt. Die schnelle Antwort eines künstlichen, neuronalen Netzes ermöglicht es dem Netzbetreiber, geeignete fehlerbehebende Schalthandlungen während kritischer Netzereignisse durchzuführen. So kann die Stabilität des elektrischen Netzes gewährleistet und bevorstehende Netzausfälle verhindert werden. Zwei offline trainierte künstliche neuronale Netze sind entwickelt worden, um a) das dynamische Verhalten unter Verwendung ausgewählter Eingangseigenschaften und b) die berechnete kritische Fehlerklärungszeit als Indikator für die transiente Stabilität und die minimale Dämpfung der Oszillationen als Indikator für ozillatorische Stabilität abzubilden. Künstliche, neuronale Netze bieten vielversprechende Lösungen für schnelle Berechnungen bei online Anwendungen. Als Folge kann die hohe Anzahl an Berechnungen, die zur Untersuchung aller zu erwartenden kritischen Netzereignissen in elektrischen Energienetzen benötigt werden, schnell durchgeführt werden. Dies ermöglicht eine Bewertung der Systemzustände des elektrischen Netzes und eine Initiierung der zu erwartenden Schalthandlungen, um so die Systemstabilität zu verbessern. Für eine genaue Bewertung der dynamischen Stabilität sollten die Eingangseigenschaften für das künstliche, neuronale Netz sorgfältig ausgewählt werden. In dieser Arbeit sind die Eingangseigenschaften aus den gesamten Systemdaten ausgewählt worden, um die folgenden Eigenschaften kennzuzeichnen: i. Veränderungen in der Systemtopologie und des Lastflusses durch Ausfälle oder planmäßige Wartungen von Hauptkomponenten des Systems, wie zum Beispiel Übertragungsleitungen, Erzeugereinheiten oder großen Lasten ii. Veränderungen des Fehlerortes und des Einflusses des Fehlers auf die elektrischen Komponenten iii. Laständerungen und Lastaufteilung zwischen Netzversorgern Die Eingangseigenschaften wurden für viele, unterschiedliche Lastszenarien in Verbindung mit den zu erwartenden Netztopologien erzeugt. Die Anfangsbedingungen sind auf Grund von Erfahrungen mit dem Betrieb von elektrischen Energienetzen und bedingt durch das zu schätzende Ziel vorausgewählt. Die endgültige Auswahl der Eingangseigenschaften ist in drei Schritte unterteilt, um so die Genauigkeit des künstlichen, neuronalen Netzes zu erhöhen, welches die dynamische Stabilität des Energienetzes abbildet. Im ersten Schritt sind die Generatorklemmenspannungseinbrüche direkt nach der Netzstörung die wichtigen ausgewählten Eigenschaften. Hierdurch wird die Schwere des kritischen Netzereignisses aus der Sicht der Erzeugungseinheit gekennzeichnet und die Fehlerstelle lokalisiert. In dem zweiten Schritt werden neue Eingangseigenschaften basierend auf der Massenträgheitskonstante des Systems und der erzeugten Leistung in jedem Gebiet berechnet. So können Veränderungen in der Netztopologie und des Lastflusses unter normalen und gestörten Betriebsbedingungen gekennzeichnet werden. Im dritten Schritt wird eine systematische Cluster-Bildung der Eigenschaften genutzt, um so die wichtigsten Eigenschaften auszuwählen, die Aussagen über die Lastzustände und den Lastfluss über die Leitungen zulassen. Alle ausgewählten Eigenschaften repräsentieren das Eingangsmuster, wobei das Ausgangsmuster der Index der dynamischen Stabilitätsanalyse ist. Die Ergebnisse stellen die Eignung des künstlichen, neuronalen Netzes bei der Bewertung der dynamischen Stabilität dar. Verbesserung der dynamischen Stabilität: Eine online Verbesserung der dynamischen Stabilität kann durch eine vorgeschlagene marktwirtschaftliche Neuplanung des deregulierten Energiesystems und durch eine Neuplanung der Erzeugungseinheiten basierend auf der Empfindlichkeitsanalyse im Falle des Betriebs des Energienetzes durch eine zentrale Einheit erreicht werden. In dem vorgeschlagenen Markt für deregulierte Energiesysteme wird im Falle, dass vorgesehenen Schalthandlungen das Netz nicht in einen stabilen Zustand zurückbringen kann, die Energie zwischen Versorgern und Verbrauchern basierend auf optionalen Leistungsgeboten umgeschichtet. Alle Erzeuger und Verbraucher sind berechtigt an diesem Markt durch freiwillige Leistungsgebote teilzunehmen, um so ihre geplante Menge chancengleich zu erhöhen oder zu verkleinern. Diese Gebote der Marktteilnehmer repräsentieren die angebotene Leistungsmenge und den darauf bezogenen Preis. Teilnehmer, von denen es verlangt ist, Erzeugung oder Verbrauch zu reduzieren, werden für diese Möglichkeit zur Reduzierung bezahlt. So kann der Verlust der Serviceleistung kompensiert werden, während Teilnehmer, deren Leistung erhöht wird, durch den Marktpreis plus zusätzlicher Kosten für zusätzliche Veränderungen entlohnt werden. Das Ziel dieser Rahmenbedingungen ist eine Verbesserung der Systemstabilität kombiniert mit einem Minimum an zusätzlichen Kosten auftretend durch die Neuplanung. Im Falle eines zentralen Energiemarktes wird die Neuplanung der Erzeuger basierend auf der Empfindlichkeitsanalyse durchgeführt, um so eine Verbesserung der Systemstabilität zu erreichen. Die Empfindlichkeitsanalyse bezieht sich auf die Systemantwort des Generators während des belastbarsten kritischen Netzereignisses. Dieses kritische Netzereignis trennt die Erzeugungseinheiten a) in kritische Maschinen, die eine positive Empfindlichkeit besitzen, und b) in nicht-kritische Maschinen mit einer negativen Empfindlichkeit. Die Einteilung in kritische und nicht-kritische Maschinen ermöglicht eine Lösung für die Stabilisierung des Systems. Die Verteilung der verschobenen Leistung zwischen den Generatoren in jeder Gruppe wird unter Verwendung der Generatorleistungen und der Massenträgheitskonstanten berechnet. Dies erleichtert den Optimierungsalgorithmus und beschleunigt das Erhalten einer möglichen Lösung. Das Ziel ist die Minimierung der Erhöhung der Kosten für die absolut erzeugte Leistung auf Grund der Abweichung vom wirtschaftlichen Arbeitspunkt. In dieser Arbeit wird die Particle Swarm Optimierung als Werkzeug verwendet, um damit eine optimale Lösung mit den minimalen Kosten zu erlangen. Dadurch kann eine Verbesserung der dynamischen Stabilität des elektrischen Energienetzes unter Berücksichtigung aller systembedingten Nebenbedingungen erlangt werden. Die Handhabung aller systembedingten Nebenbedingungen inklusive der Nebenbedingungen der dynamischen Stabilität kann durch eine selbstanpassende Straffunktion erreicht werden

Multi-agent coalition formation in power transmission planning

Deregulation and restructuring have become unavoidable trends to the power industry recently in order to increase its efficiency, to reduce operation costs, or to provide customers better services. The once centralized system planning and management must be remodeled to reflect the changes in the market environment. We have proposed and developed a multi-agent based system to assist players, such as, owners of power generation stations, owners of transmission lines, and groups of consumers, in the same market to select partners to form coalitions. The system provides users with a cooperation plan and its associated cost allocation plan for the users to support their decision making process. Bilateral Shapley Value (BSV) was selected as the theoretical foundation to develop the system. The multi-agent system was developed by the combination of IDEAS and Tcl/Tk.published_or_final_versio