Polyhedral Predictive Regions For Power System Applications

Despite substantial improvement in the development of forecasting approaches, conditional and dynamic uncertainty estimates ought to be accommodated in decision-making in power system operation and market, in order to yield either cost-optimal decisions in expectation, or decision with probabilistic guarantees. The representation of uncertainty serves as an interface between forecasting and decision-making problems, with different approaches handling various objects and their parameterization as input. Following substantial developments based on scenario-based stochastic methods, robust and chance-constrained optimization approaches have gained increasing attention. These often rely on polyhedra as a representation of the convex envelope of uncertainty. In the work, we aim to bridge the gap between the probabilistic forecasting literature and such optimization approaches by generating forecasts in the form of polyhedra with probabilistic guarantees. For that, we see polyhedra as parameterized objects under alternative definitions (under $L_1$ and $L_\infty$ norms), the parameters of which may be modelled and predicted. We additionally discuss assessing the predictive skill of such multivariate probabilistic forecasts. An application and related empirical investigation results allow us to verify probabilistic calibration and predictive skills of our polyhedra.Comment: 8 page

Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power

In the probabilistic energy forecasting literature, emphasis is mainly placed on deriving marginal predictive densities for which each random variable is dealt with individually. Such marginals description is sufficient for power systems related operational problems if and only if optimal decisions are to be made for each lead-time and each location independently of each other. However, many of these operational processes are temporally and spatially coupled, while uncertainty in photovoltaic (PV) generation is strongly dependent in time and in space. This issue is addressed here by analysing and capturing spatio-temporal dependencies in PV generation. Multivariate predictive distributions are modelled and space-time trajectories describing the potential evolution of forecast errors through successive lead-times and locations are generated. Discrimination ability of the relevant scoring rules on performance assessment of space-time trajectories of PV generation is also studied. Finally, the advantage of taking into account space-time correlations over probabilistic and point forecasts is investigated. The empirical investigation is based on the solar PV dataset of the Global Energy Forecasting Competition (GEFCom) 2014.Comment: 33 pages, 11 Figure

Ellipsoidal Prediction Regions for Multivariate Uncertainty Characterization

While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times and variables of interest. Such dependencies are key in a large share of operational problems involving renewable power generation, load and electricity prices for instance. The few methods that account for dependencies translate to sampling scenarios based on given marginals and dependence structures. However, for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of polyhedra or ellipsoids. Consequently, we propose a systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predefined probability and minimum volume. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for misspecification of ellipsoidal prediction regions. Application results based on three datasets with wind, PV power and electricity prices, allow us to assess the skill of the resulting ellipsoidal prediction regions, in terms of calibration, sharpness and overall skill.Comment: 8 pages, 7 Figures, Submitted to IEEE Transactions on Power System

Uncertainty quantification of photovoltaic power generation

Environmental concerns and global energy crises have caused a rapid proliferation of renewable energy sources worldwide. For Singapore, due to its geographical location within the tropical Sun Belt, PV energy is the most propitious kind of renewables that can be deployed. Two important issues related to intermittency of PV generation are variability and uncertainty. Variable power with uncertain nature has several consequences in power systems such as the need for rising ancillary service, increase in voltage and frequency fluctuations and adverse effects on power quality and economics. What exacerbates the situation is the lack of inertia in PV systems which leads to abrupt changes in voltages and power outputs. Improving PV forecasting methods, as an indispensable approach to mitigate PV power intermittency, has attracted researchers recently. Forecasts can be done as point or probabilistic predictions. Nearly all previous works focused on single-valued (or point) forecasts, similar to the case of wind power forecasting. However, point forecasts can only be helpful when no significant uncertainty is involved, since it fails to dispense a full picture of all potential future outcomes. On the other hand, the aim of probabilistic predictions is to provide decision-making under uncertainty with the full information. In recent years, there has been a surge of interest in stochastic optimization approaches to cover different uncertainties in power systems. It is often assumed that the random variables involved have known parametric distributions. However, even in cases where observations form a known and well-behaved marginal distribution, there is no guarantee that conditional predictive densities (or distributions of forecast errors) follow that same distribution. Wrong distributional assumptions may directly yield biases in analyses and results. Stochastic optimization therefore calls for a thorough design and evaluation of probabilistic forecasting approaches. Although valuable studies have been conducted on probabilistic wind forecasting, one can hardly find published investigations on probabilistic solar energy forecasts. In spite of the similarities in wind and PV power forecasts, there are significant differences such as the influential variables and the relationship between meteorological variables and the available PV power which may result in different statistical features. This, indeed, necessitates a more focused research on solar power statistics. This thesis aims at providing practical methods for both point and probabilistic forecasts of PV generation. If probabilistic forecasts are properly employed, they can serve as a decision-aiding tool to alleviate challenges attached to stochastic generation. However, despite of the benefits of probabilistic forecasts over point forecasts, they fail to capture development of forecast errors through successive lead-times, interdependent generation in contiguous locations or negatively correlated generation levels in diverse geographic areas. The reason is that they treat random variables for each lead-time and each location individually and separately while PV generations are stochastic processes with spatially spread and time interdependent infeeds. Therefore, in multi-stage decision-making problems such as unit-commitment or optimal power flow, it is an integral requirement to estimate aggregated uncertainties in the system and model space-time stochasticity of intermittent resources. This issue is addressed here by analysing and capturing spatio-temporal dependencies in PV generation. Multivariate predictive distributions are modelled and space-time trajectories describing the potential evolution of forecast errors through successive lead-times and locations are generated.Doctor of Philosophy (EEE

Probabilistic energy management of a renewable microgrid with hydrogen storage using self-adaptive charge search algorithm

Micro Grids (MGs) are clusters of the DER (Distributed Energy Resource) units and loads which can operate in both grid-connected and island modes. This paper addresses a probabilistic cost optimization scheme under uncertain environment for the MGs with several multiple Distributed Generation (DG) units. The purpose of the proposed approach is to make decisions regarding to optimizing the production of the DG units and power exchange with the upstream network for a Combined Heat and Power (CHP) system. A PEMFCPP (Proton Exchange Membrane Fuel cell power plant) is considered as a prime mover of the CHP system. An electrochemical model for representation and performance of the PEMFC is applied. In order to best use of the FCPP, hydrogen production and storage management are carried out. An economic model is organized to calculate the operation cost of the MG based on the electrochemical model of the PEMFC and hydrogen storage. The proposed optimization scheme comprises a self-adaptive Charged System Search (CSS) linked to the 2m + 1 point estimate method. The 2m + 1 point estimate method is employed to cover the uncertainty in the following data: the hourly market tariffs, electrical and thermal load demands, available output power of the PhotoVoltaic (PV) and Wind Turbines (WT) units, fuel prices, hydrogen selling price, operation temperature of the FC and pressure of the reactant gases of FC. The Self-adaptive CSS (SCSS) is organized based on the CSS algorithm and is upgraded by some modification approaches, mainly a self-adaptive reformation approach. In the proposed reformation method, two updating approaches are considered. Each particle based on the ability of those approaches to find optimal solutions in the past iterations, chooses one of them to improve its solution. The effectiveness of the proposed approach is verified on a multiple-DG MG in the grid-connected mode

Design an optimized power system stabilizer using NSGA-II based on fuzzy logic principle

Damping of electro-mechanical oscillations in interconnected power systems is important to guarantee secure and stable operation of the system. Power system stabilizers (PSSs) are applied to damp these oscillations. To overcome flows associated with conventional algorithm of PSS design, a modified version of non-dominated sorting genetic algorithm is used to regulate the PSS parameters in this paper. Additionally, fuzzy logic principle is employed to develop PSS in order to improve the stability and reliability of the power systems subjected to disturbances. Two-area (four-machine) power system is considered as the case study in this paper. PSS parameters are obtained for four PSS connected to four generators. The effectiveness of the proposed algorithm in damping the system oscillations during overall disturbances is evaluated. The simulation results illustrate this issue

Direct torque control of brushless DC motor drives based on ANFIS controller with fuzzy supervisory learning

This paper presents a direct torque control technique for brushless DC motors with non-sinusoidal back electromotive force. Direct torque control has some benefits such as faster torque response and reduced torque ripple for driving the brushless DC motors. Because of ignoring stator flux linkage of direct torque control in constant torque region, driving of brushless DC motors is not complicated. In order to solve the problems associated with conventional PI speed controller, a new speed control based on adaptive-neuro fuzzy inference system is proposed to eliminate overshoot in the torque and speed responses, simplify designing and reduce complexity of math formulas. To reduce rising time, PD controller is used with adaptive-neuro-based Fuzzy Inference System. The effectiveness of proposed system has been validated by simulation results