Long-Term Economic Model for Allocation of FACTS Devices in Restructured Power System Integrated Wind Generation

This study proposes an approach to optimally allocate multiple types of flexible AC transmission system (FACTS) devices in market-based power systems with wind generation. The main objective is to maximise profit by minimising device investment cost, and the system's operating cost considering both normal conditions and possible contingencies. The proposed method accurately evaluates the long-term costs and benefits gained by FACTS devices (FDs) installation to solve a large-scale optimisation problem. The objective implies maximising social welfare as well as minimising compensations paid for generation re-scheduling and load shedding. Many technical operation constraints and uncertainties are included in problem formulation. The overall problem is solved using both particle swarm optimisations for attaining optimal FDs allocation as main problem and optimal power flow as sub-optimisation problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

Irish Power System Primary Frequency Response Metrics during Different System Non Synchronous Penetrations.

This paper discusses the use of primary frequency response metrics to assess the dynamics of frequency disturbance data with the presence of high system non synchronous penetration (SNSP) and system inertia variation. The Irish power system has been chosen as a study case as it experiences a significant level of SNSP from wind turbine generation and imported active power from HVDC interconnectors. Several recorded actual frequency disturbances were used in the analysis. These data were measured and collected from the Irish power system from October 2010 to June 2013. The paper has shown the impact of system inertia and SNSP variation on the performance of primary frequency response metrics, namely: nadir frequency, rate of change of frequency, inertial and primary frequency response.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Optimizing the renewable generation mix in the portuguese power system based on temporal and spatial diversity

Renewable energy sources (RES) have unique characteristics that grant them preference in energy and environmental policies. However, considering that the renewable resources are barely controllable and sometimes unpredictable, some challenges are faced when integrating high shares of renewable sources in power systems. In order to mitigate this problem, this paper presents a decision-making methodology regarding renewable investments. The model computes the optimal renewable generation mix from different available technologies (hydro, wind and photovoltaic) that integrates a given share of renewable sources, minimizing residual demand variability, therefore stabilizing the thermal power generation. The model also includes a spatial optimization of wind farms in order to identify the best distribution of wind capacity. This methodology is applied to the Portuguese power system.

The Hydro-Electric Power Commission of Ontario: address. (1923)

The Public Ownership League of America Conference, September 10th to 13th, 1923, at Toronto, Ontario.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Hybrid Power System Modelling - Simulation and Energy Management Unit Development

Weltweit leben mehr als 2 Milliarden Menschen in ländlichen Gebieten. Als Konzept für die elektrische Energieversorgung solcher Gebiete kommen dezentrale elektrische Energieversorgungseinheiten zum Einsatz, die lokal verfügbare erneuerbare Ressourcen nutzen. Stand der Technik bilden Einheiten, die auf PV-Diesel-Batterie System basieren. Die verwendeten Versorgungsskonzepte in Hybridsystemen sind durch den Einsatz von Batterien als Energiespeicher meist wenig zuverlässig und teuer. Diese Energiespeicher sind sehr aufwendig zu überwachen und schwerig zu entsorgen. Den Schwerpunkt dieser Arbeit bildet die Entwicklung eines neuen Hybridsystems mit einem Wasserreservoir als Energiespeicher. Dieses Konzept eignet sich für Bergregionen in Entwicklungsländern wie Nepal, wo z.B. neben der solaren Strahlung kleine Flüsse in großer Anzahl vorhanden sind. Das Hybridsystem verfügt über einen Synchrongenerator, der die Netzgrößen Frequenz und Spannung vorgibt und zusätzlich unterstützen PV und Windkraftanlage die Versorgung. Die Wasserkraftanlage soll den Anteil der erneuerbaren Energienutzung erhöhen. Die Erweiterung des Systems um ein Dieselaggregat soll die Zuverlässigkeit der Versorgung erhöhen. Das Hybridsystem inkl. der Batterien wird modelliert und simuliert. Anschließend werden die Simulations- und Messergebnisse verglichen, um eine Validierung des Modells zu erreichen. Die Regelungsstruktur ist aufgrund der hohen Anzahl an Systemen und Parametern sehr komplex. Sie wird mit dem Simulationstool Matlab/Simulink nachgebildet. Das Verhalten des Gesamtsystems wird unter verschiedene Lasten und unterschiedlichen meteorologischen Gegebenheiten untersucht. Ein weiterer Schwerpunkt dieser Arbeit ist die Entwicklung einer modularen Energiemanagementeinheit, die auf Basis der erneuerbaren Energieversorgung aufgebaut wird. Dabei stellt die Netzfrequenz eine wichtige Eingangsgröße für die Regelung dar. Sie gibt über die Wirkleistungsstatik die Leistungsänderung im Netz wider. Über diese Angabe und die meteorologischen Daten kann eine optimale wirtschaftliche Aufteilung der Energieversorgung berechnet und eine zuverlässige Versorgung gewährleistet werden. Abschließend wurde die entwickelte Energiemanagementeinheit hardwaretechnisch aufgebaut, sowie Sensoren, Anzeige- und Eingabeeinheit in die Hardware integriert. Die Algorithmen werden in einer höheren Programmiersprache umgesetzt. Die Simulationen unter verschiedenen meteorologischen und netztechnischen Gegebenheiten mit dem entwickelten Model eines Hybridsystems für die elektrische Energieversorgung haben gezeigt, dass das verwendete Konzept mit einem Wasserreservoir als Energiespeicher ökologisch und ökonomisch eine geeignete Lösung für Entwicklungsländer sein kann. Die hardwaretechnische Umsetzung des entwickelten Modells einer Energiemanagementeinheit hat seine sichere Funktion bei der praktischen Anwendung in einem Hybridsystem bestätigen können.

Implications of the North Atlantic Oscillation for a UK–Norway renewable power system

UK wind-power capacity is increasing and new transmission links are proposed with Norway, where hydropower dominates the electricity mix. Weather affects both these renewable resources and the demand for electricity. The dominant large-scale pattern of Euro-Atlantic atmospheric variability is the North Atlantic Oscillation (NAO), associated with positive correlations in wind, temperature and precipitation over northern Europe. The NAO's effect on wind-power and demand in the UK and Norway is examined, focussing on March when Norwegian hydropower reserves are low and the combined power system might be most susceptible to atmospheric variations. The NCEP/NCAR meteorological reanalysis dataset (1948–2010) is used to drive simple models for demand and wind-power, and ‘demand-net-wind’ (DNW) is estimated for positive, neutral and negative NAO states. Cold, calm conditions in NAO− cause increased demand and decreased wind-power compared to other NAO states. Under a 2020 wind-power capacity scenario, the increase in DNW in NAO− relative to NAO neutral is equivalent to nearly 25% of the present-day average rate of March Norwegian hydropower usage. As the NAO varies on long timescales (months to decades), and there is potentially some skill in monthly predictions, we argue that it is important to understand its impact on European power systems.

Analysis and manipulation of the structure of odor plumes from a piezo-electric release system and measurements of upwind flight of male Almond Moths, Cadra cautella, to pheromone plumes

We investigated the plume structure of a piezo-electric sprayer system, set up to release ethanol in a wind tunnel, using a fast response mini-photoionizaton detector. We recorded the plume structure of four different piezo-sprayer configurations: the sprayer alone; with a 1.6-mm steel mesh shield; with a 3.2-mm steel mesh shield; and with a 5 cm circular upwind baffle. We measured a 12 × 12-mm core at the center of the plume, and both a horizontal and vertical cross-section of the plume, all at 100-, 200-, and 400-mm downwind of the odor source. Significant differences in plume structure were found among all configurations in terms of conditional relative mean concentration, intermittency, ratio of peak concentration to conditional mean concentration, and cross-sectional area of the plume. We then measured the flight responses of the almond moth, Cadra cautella, to odor plumes generated with the sprayer alone, and with the upwind baffle piezo-sprayer configuration, releasing a 13:1 ratio of (9Z,12E)-tetradecadienyl acetate and (Z)-9-tetradecenyl acetate diluted in ethanol at release rates of 1, 10, 100, and 1,000 pg/min. For each configuration, differences in pheromone release rate resulted in significant differences in the proportions of moths performing oriented flight and landing behaviors. Additionally, there were apparent differences in the moths’ behaviors between the two sprayer configurations, although this requires confirmation with further experiments. This study provides evidence that both pheromone concentration and plume structure affect moth orientation behavior and demonstrates that care is needed when setting up experiments that use a piezo-electric release system to ensure the optimal conditions for behavioral observations.

Increasing thermal plant flexibility in a high renewables power system

Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability. This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

Electric Power Service Areas, Energy Lines, and Hydroelectric Dams of Maine

http://digitalcommons.colby.edu/atlasofmaine2008/1010/thumbnail.jpg

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.