Electricity Distribution

A methodology for carrying out web-based power systems simulations using PHP programming for the simulation engine is described in this chapter. Power system simulation is essential for planning and studying how an electrical network system will operate over time without physically assembling it. The methodology is implemented in a modular object-oriented PHP application that computes the power flow solution of electrical networks using the Newton-Raphson method. A key difference between this solution and existing web-based power systems simulation applications is in the architecture; other solutions use a 3-tier structure: web browser, web server scripts and simulation engine while this has the simulation engine running in the web server scripts thereby creating a slimmer 2-tier structure. This 2-tier structure and the choice of PHP has considerable implications in terms of server resources required to execute the solution, which is increasingly important in this era of cloud computing, Software-as-a-Service (SaaS) and smart electricity networks. The methodology covers the more recent features of PHP that make it possible to carry out such analysis which were not present in previous versions of the language. It also covers how additional classes required to provide mathematical functionality not present in the language core can be used to build the simulation engine. The methodology provides a viable option for carrying out fundamental power systems studies using open source software.

Current paper deals with three main issues, regarding the operation of the electric power systems in the presence of distributed generation. The first issue regards the assessment of the main continuity of supply parameters, such as frequency of interruptions, duration of interruption, power not served and energy no served. This issues are the base guidelines for the network system operators’ decision of building new distributed generation capacities which might be able to mitigate the electric network loss of operation. The second issue addressed was the consume variability of the various low-voltage consumers. This particularity, in conjunction with the sheer number of consumers leads to the creation of serious unbalance for the daily curve. This means that if the network system operator cannot contain the load variability, it must buy energy from intraday energy market, with a price decisively higher than the one of the reserved capacity. This issue leads to a two-fold necessity: implementing a better know-how system in order to better contain the customers load fluctuation and the necessity of distributed generation implementation, in order to help load shedding. The third issue regards general technical aspects with respect to the electrical distribution systems operation in the presence of distributed generation.

The current work presents an overview of islanding detection techniques, highlighting their advantages and disadvantages. Generally, all anti-islanding techniques detect the absence of utility-controlled generation and stop energy production. However, when the generation (from PVs) and loads within the island segment are well balanced—prior to the isolation event—it is difficult to detect the utility absence. The performed analysis indicates the fact that the islanding detection is a complicated procedure, which is affected by various parameters (load matching, quality factor, PV actual generation etc.). The islanding prevention techniques which are elaborated in this paper are limited to the case where the island segment belongs to the LV distribution grids, while remote islanding detection techniques are out of scope. Furthermore, this work presents an overview of the evaluation of the islanding detection techniques according to IEC 62116 and IEEE Std. 929-2000 standards. Finally, results from the experimental evaluation of various islanding detection techniques from different single phase inverters are presented too.

As the Smart Grid has the potential to bring significant value to the various stakeholders of the electricity market, a methodology for the evaluation of the smart grid benefits related to distributed generation is required to facilitate decision making. This chapter proposes a generic framework to assess these benefits using as a study case an autonomous distributed photovoltaic generation system in a Greek island that employs powerline communications technology.

This chapter identifies the correlation between renewable electricity generation and electricity demand using as a case study Portugal. It presents the Portuguese current electric system, the installed generation capacity, and the electricity demand pattern for the year 2012. It also presents the 2020 national strategy for this sector, namely the Renewables Plan of Action, its targets and challenges. It focuses on the three main natural resources that exist in the country and describes their technology as well as the technical challenges for the integration of renewable generation in the electric system. Through this study the effectiveness of hydro, solar and wind power to meet electricity demand in Portugal is investigated. Statistical data regarding the seasonal and daily availability of the three main resources, relating them with the electricity generated from those sources and their correspondent capacity factors are presented. In addition, comparative analyses are performed between the electricity demand curve, both seasonal and daily, and statistical data related to the electricity generation from the renewable sources using both the Pearson product-moment correlation coefficient and graphical illustrations. The results obtained confirm a correlation between the renewables availability, namely, hydro and wind power, and electricity demand during a typical year. They also suggest no correlation between demand and a solar/wind combination during a 24-h period, however, they reveal a complementarity between solar and wind power availability during a typical day, highlighting the need and advantages of energy storage systems and “smart grid” technologies, to adjust electricity generation curves to demand load curves. The authors strongly believe that this study can be useful to the development of national strategies for the modern electric power systems.

Learning control is an iterative approach to the problem of output tracking for processes that are repetitive in nature and operate over fixed time intervals. The adaptability of the control law to changes of model parameters, to unmodelled dynamics and to nonlinearities of real systems has been proven valuable for applications in management and control of components of production and distribution networks. The ILC problem is converted to the design problem of the linear approximation parameters of the input function in a subspace spanned by a set of basis functions. Results on positive invariant sets of linear discrete-time systems allow for the development of a robust ILC algorithm that deals with the uncertainties in the systems model and the restriction of the input and output spaces. The method is presented in view of applications in the management and control of components of production and distribution networks such as Active Power Filters, Inverters for interfacing with renewable energies microgrids and wind turbines.

This chapter presents a study of the small signal stability applied to an electric power system, with the consideration of the Power System Stabilizer and using the optimal control theory. A new technique is proposed, which is based on pole placement using optimal state feedback for damping electromechanical oscillation under small signal. The proposed technique builds the weighting matrices of the quadratic terms for the state vector Q and control vector R in such a way that the system response also obeys conventional criteria for the system pole location. Besides, when the number of output variables is less than the order of the system, it is proposed an optimal output feedback approach, where a set of closed-loop system poles is allocated to an arbitrary position by means of a suitable output feedback. The Power Sensitivity Model is used to represent the electric power system. Information about the stability of the electric power system, when subjected to small disturbances, is illustrated by using numerical examples.

This paper presents an LCL-filters design and control for three-phase PWM voltage source grid inverter. The main objective is to achieve optimum damping with a desired system control bandwidth for the LCL-filter. This control algorithm is implemented by using Bacteria Foraging Optimization. Mathematical analysis has been presented to study the steady-state and dynamic performances of the overall system. Only one set of current sensors is required for the feedback control. The proposed system is simulated and results illustrated that bacterial foraging optimization is a skilful clarification for realizing the best parameters of LCL-filters and the PI current controller. Experimental results endorse the proposed technique and highlight its practicability.

Power transformers in the power electric system are of particular significance for the reliability of electric power consumers. Up-to-date monitoring systems for power transformers support taking measurements and analysis of: temperature of the hot spots in the winding (calculated automatically by the system); gas and moisture contents in the oil inside transformer’s tank; partial discharge activity; winding insulation humidity and other important parameters, but do not render an account of the small amplitude of partial discharges availability under 300 pC. In this article a method for locating the place of origin of the partial discharges in the three-dimensional space is described. Future efforts are to be dedicated on finding an algorithm for on-line measurement and locating of the partial discharge from incipient fault.

An advanced assessment of voltage sag characteristics requires analytical mathematical expressions extracted from proper short-circuit analysis. In this document, the procedure for the extraction of those expressions is analytically described. All parameters affecting the response of a power network to a fault are taken into account. The expressions extracted enable the calculation of the during-fault voltage vector for a fault not only at the network’s buses but also at every position within the network. They also enable the drawing of the voltage magnitude or the phase-angle jump in relation with the distance to the fault from the one power line end. Moreover, a simple and effective way to incorporate the phase shift introduced by devices or transformers is proposed. A test network is used for the demonstration of the effectiveness of those expressions and the understanding of voltage sag characteristics.

In this work, the Automatic Generation Control of an interconnected hydrothermal power system with Superconducting Magnetic Energy Storage (SMES) has been investigated. The gain settings of integral controller as well as Proportional Integral Derivative (PID) controller are tuned by Integral Time Squared Error (ITSE) criterion using Genetic Algorithm. Simulation, comparison and analysis of dynamic performances in the presence of Generation Rate Constraints brings out the superior performance of SMES units and PID controller in suppressing frequency and inter area tie line power deviations from their nominal values followed by a step load disturbance in thermal area.

This chapter presents an Attribute Grammar capable of modelling power system signals. Primitive pattern selection, linguistic representation, and pattern grammar formulation are the sub problems of tackled. The recognition of power system waveforms and to the measurement of its parameters is proven to easily be handled using syntactic pattern recognition techniques. Attribute grammars are used as the model for the pattern grammar because of their descriptive power, which is due to their ability to handle syntactic as well as semantic information. In order the functionality of the proposed system to be tested, a software implementation has been developed using waveforms and data provided by the Independent Power Transmission Operator (IPTO) in Greece. The proposed methodology will be applied to the implementation of an efficient protective relay that would efficiently prevent safety problems and economic losses caused by faults presented in power systems.

In this chapter, a multi-layer perceptron neural network has been developed for prediction of nodal prices at various buses of power system under restructured environment. Levenberg-Marquardt algorithm has been applied to speed up the training of the multi-layer feed-forward neural network. To select the effective inputs for the Levenberg-Marquardt algorithm based artificial neural network (LMANN), an unsupervised vector quantization based clustering technique has been applied. Effectiveness of the proposed LMANN based approach for nodal price prediction has been demonstrated on benchmark 6-bus system and RTS 24-bus system. Since the training of artificial neural network is extremely fast and test results are accurate, they can be directly floated to OASIS (open access same time information system) web site. The Market Participants willing to make transactions can access this information instantly.