Optimal Placement of Capacitors in Radial Distribution System to Minimizes the Losses at Variable Load Levels

This paper presents an efficient approach for optimal placement of capacitor in radial distribution system, with an objective of improving the voltage profile and reduction of power loss. Distribution system is a link between high voltage transmission system and the low voltage end users, so it’s very essential to keep the system healthy and with minimum losses. The present work is devoted to determine the optimal locations and sizes of capacitors with different load levels using a genetic algorithm. Implementation of the Genetic Algorithm for its multifunction capability is one of the distinguished characteristics for optimal capacitor placement in distribution system. To verify the effectiveness of the proposed algorithm, it is tested on IEEE 33-bus radial distribution networks

Optimization of the operation of a distribution network with distributed generation using genetic algorithm

Aquesta tesi analitza la integraci o de generaci o basada en fonts d'energia renovable i com aquesta afecta a l'operaci o de la regulaci o de tensi o i pot encia reactiva en una xarxa de distribuci o. En un primer lloc, s'han estudiat una selecci o de les tecnologies de generaci o distribu da. S'han analitzat les fonts d'energia emprades per a la generaci o d'electricitat, com l'energia e olica, hidroel ectrica, mareomotriu, geot ermica i plantes t ermiques i la tecnologia que fan servir per integrar-se en la xarxa el ectrica. Una de les q uestions m es importants a tenir en compte en els sistemes el ectrics es la regulaci o de la tensi o i la pot encia reactiva. Per aix o, s'ha estudiat com varia la tensi o en els sistemes el ectrics i que dispositius s'empren per limitar les conseq u encies de variacions elevades de tensi o. Les an alisis en r egim permanent dels sistemes el ectrics es duen a terme a trav es d'an alisis de uxos de c arregues. En aquesta tesi s'han estudiat alguns dels m etodes existents i s'ha realitzat una comparaci o entre ells per tal triar un m etode per dur a terme l'estudi objecte de la tesi. Quan es modela matem aticament un sistema el ectric, hi ha algunes q uestions que s'han de tenir en compte. Aspectes com els requeriments de control, e ci encia energ etica o el retorn de la inversi o s'estableixen les bases dels problemes d'optimitzaci o. La resoluci o dels problemes d'optimitzaci o s'aconsegueix quan es troba un valor acceptable per a una funci o objectiu que a m es est a subjecte a limitacions. En aquesta tesi s'ha modelat un sistema el ectric basat en un sistema est andard i s'ha realitzat una an alisi del ux de c arregues per a diferents escenaris. S'ha estudiat l'impacte que la generaci o distribu da t e sobre el sistema. S'han implementat diferents algoritmes d'optimitzaci o basats en el principi de la selecci o natural, per resoldre q uestions com la localitzaci o, el nivell de generaci o o el control del factor de pot encia per part dels generadors connectats. Tota la formulaci o matem atica i els algoritmes d'optimitzaci o s'han realitzat mitjan cant el programa Matlab / Simulink

Operational Planning and Optimisation in Active Distribution Systems for Flexible and Resilient Power

The electricity network is undergoing significant changes to cater to environmental-deterioration and fuel-depletion issues. Consequently, an increasing number of renewable resources in the form of distributed generation (DG) are being integrated into medium-voltage distribution networks. The DG integration has created several technical and economic challenges for distribution network operators. The main challenge is basically the problem of managing network voltage profile and congestion which is caused by increasing demand and intermittent DG operations. The result of all of these changes is a paradigm shift in the way distribution networks operate (from passive to active) and are managed that is not limited only to the distribution network operator but actively engages with network users such as demand aggregators, DG owners, and transmission-system operators. This thesis expands knowledge on the active distribution system in three specific areas and attempts to fill the gaps in existing approaches. A comprehensive active network management framework in active distribution systems is developed to allow studies on (i) the flexibility of network topology using modern power flow controllers, (ii) the benefits of centralised thermal electricity storage in achieving the required levels of flexibility and resiliency in an active distribution system, and (iii) system resiliency toward fault occurrence in hybrid AC/DC distribution systems. These works are implemented within the Advanced Interactive Multidimensional Modelling Systems (AIMMS) software to carry out optimisation procedure. Results demonstrate the benefit provided by a range of active distribution system solutions and can guide future distribution-system operators in making practical decisions to operate active distribution systems in cost-effective ways

Planning and Operation of DSTATCOM in Electrical Distribution Systems

In present day scenario, it is most essential to consider the maximum asset performance of the power distribution systems to reach the major goals to meet customer demands. To reach the goals, the planning optimization becomes crucial, aiming at the right level of reliability, maintaining the system at a low total cost while keeping good power quality. There are some problems encountered which are hindering the effective and efficient performance of the distribution systems to maintain power quality. These problems are higher power losses, poor voltage profile near to the end customers, harmonics in load currents, sags and swells in source voltage etc. All these problems may arise due to the presence of nonlinear loads, unpredictable loads, pulse loads, sensor and other energy loads, propulsion loads and DG connections etc. Hence, in order to improve the power quality of power distribution systems, it is required to set up some power quality mitigating devices, for example, distribution static synchronous compensator (DSTATCOM), dynamic voltage restorer (DVR), and unified power quality conditioner (UPQC) etc. The goal of this project work is to devise a planning of optimal allocation of DSTATCOM in distribution systems using optimization techniques so as to provide reactive power compensation and improve the power quality

A Review of Active Management for Distribution Networks: Current Status and Future Development Trends

Driven by smart distribution technologies, by the widespread use of distributed generation sources, and by the injection of new loads, such as electric vehicles, distribution networks are evolving from passive to active. The integration of distributed generation, including renewable distributed generation changes the power flow of a distribution network from unidirectional to bi-directional. The adoption of electric vehicles makes the management of distribution networks even more challenging. As such, an active network management has to be fulfilled by taking advantage of the emerging techniques of control, monitoring, protection, and communication to assist distribution network operators in an optimal manner. This article presents a short review of recent advancements and identifies emerging technologies and future development trends to support active management of distribution networks

Multi-objective power quality optimization of smart grid based on improved differential evolution

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Stochastic power system optimisation algorithm with applications to distributed generation integration

PhD ThesisThe ever increasing level of penetration of Distributed Generation (DG) in power distribution networks is not without its challenges for network planners and operators. Some of these challenges are in the areas of voltage regulation, increase of network fault levels and the disturbance to the network protection settings. Distributed generation can be beneficial to both electricity consumers and if the integration is properly engineered the energy utility. Thus, the need for tools considering these challenges for the optimal placement and sizing of DG units cannot be over emphasized. This dissertation focuses on the application of a soft computing technique based on a stochastic optimisation algorithm (Particle Swarm Optimisation or PSO) for the integration of DG in a power distribution network. The proposed algorithm takes into consideration the inherent nature of the control variables that comprise the search space in the optimal DG sizing/location optimisation problem, without compromising the network operational constraints. The developments of the proposed Multi-Search PSO algorithm (MSPSO) is described, and the algorithm is tested using a standard, benchmarking 69-bus radial distribution network. MSPSO results and performance are compared with that of a conventional PSO algorithm (and other analytical and stochastic methods). Both single-objective (minimising network power loss) and multi-objective (considering nodal voltages as part of the cost function) optimisation studies were conducted. When compared with previously published studies, the proposed MSPSO algorithm produces more realistic results since it accounts for the discrete sizes of commercially available DG units. The new MSPSO algorithm was also found to be the most computationally efficient, substantially reducing the search space and hence the computational cost of the algorithm compared with other methods, without loss of quality in the obtained solutions. As well as the size and location of DG units, these studies considered the operation of the generators to provide ancillary voltage support to the network (i.e. with the generators operating over a realistic range of lagging power factors, injecting reactive power into the network). The algorithm was also employed to optimise the integration of induction generation based DG into the network, considering network short-circuit current ratings and line loading constraints. A new method for computing the reactive power requirement of the Abstract V induction generator (based on the machine equivalent circuit) was developed and interfaced with the MSPSO to solve the optimization problem, including the generator shunt compensation capacitors. Finally, the MSPSO was implemented to carry out a DG integration problem for a real distribution network and the results validated using a commercial power system analysis tool (ERACS).Petroleum Technology Development Fund (PTDF) Overseas Scholarship Schem

Hybridization of particle Swarm Optimization with Bat Algorithm for optimal reactive power dispatch

This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based approach to solve Optimal Reactive Power Dispatch (ORPD) problem. The primary objective of this project is minimization of the active power transmission losses by optimally setting the control variables within their limits and at the same time making sure that the equality and inequality constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA) algorithms which are nature-inspired algorithms have become potential options to solving very difficult optimization problems like ORPD. Although PSO requires high computational time, it converges quickly; while BA requires less computational time and has the ability of switching automatically from exploration to exploitation when the optimality is imminent. This research integrated the respective advantages of PSO and BA algorithms to form a hybrid tool denoted as HPSOBA algorithm. HPSOBA combines the fast convergence ability of PSO with the less computation time ability of BA algorithm to get a better optimal solution by incorporating the BA’s frequency into the PSO velocity equation in order to control the pace. The HPSOBA, PSO and BA algorithms were implemented using MATLAB programming language and tested on three (3) benchmark test functions (Griewank, Rastrigin and Schwefel) and on IEEE 30- and 118-bus test systems to solve for ORPD without DG unit. A modified IEEE 30-bus test system was further used to validate the proposed hybrid algorithm to solve for optimal placement of DG unit for active power transmission line loss minimization. By comparison, HPSOBA algorithm results proved to be superior to those of the PSO and BA methods. In order to check if there will be a further improvement on the performance of the HPSOBA, the HPSOBA was further modified by embedding three new modifications to form a modified Hybrid approach denoted as MHPSOBA. This MHPSOBA was validated using IEEE 30-bus test system to solve ORPD problem and the results show that the HPSOBA algorithm outperforms the modified version (MHPSOBA).Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Optimal placement of statcom controllers with metaheuristic algorithms for network power loss reduction and voltage profile deviation minimization.

Masters Degree. University of KwaZulu-Natal, Durban.Transmission system is a series of interconnected lines that enable the bulk movement of electrical power from a generating station to an electrical substation. This system suffers from unavoidable power losses and consequently voltage profile deviation which affects the overall efficiency of the system; hence the need to reduce these losses and voltage magnitude deviations. The existing methods of incorporation of static synchronous compensator (STATCOM) controllers to solve these problems suffer from incorrect location and sizing, which could bring about insignificant reduction in transmission network losses and voltage magnitude deviations. Hence, this research aims to reduce transmission network losses and voltage magnitude deviation in transmission network by suitable allocation of STATCOM controller using firefly algorithm (FA) and particle swarm optimization (PSO). A mathematical steady-state STATCOM power injection model was formulated from one voltage source representation to generate new set of equations, which was incorporated into the Newton-Raphson (NR) load flow solution algorithm and then optimized using PSO and FA. The approach was applied to IEEE 14-bus network and simulations were performed using MATLAB program. The results showed that the best STATCOM controller locations in the system after optimization were at bus 11 and 9 with the injection of shunt reactive power of 8.96 MVAr, and 9.54 MVAr with PSO and FA, respectively. The total active power loss for the network under consideration at steady state, with STATCOM only and STATCOM controller optimized using PSO and FA, were 6.251 MW, 6.075 MW, 5.819 MW and 5.581 MW, respectively. The corresponding reactive power were 14.256 MVAr, 13.857 MVAr, 12.954 MVAr and 12.156 MVAr, respectively. In addition, bus voltage profile improvement indicates the effectiveness of metaheuristic methods of STATCOM optimization. However, FA gave a better power loss and voltage magnitude deviations minimizations over PSO. The study concluded that FA is more effective as an optimization technique for suitably locating and sizing of STATCOM controller on a power transmission system.Publications listed on page iii

Voltage control strategies for loss minimzation in autonomous microgrids

This dissertation investigates the novel idea of flexible-voltage autonomous microgrids (MG), employing several interconnectable dc buses operating in a minimum-voltage mode. In comparison with the traditional fixed-voltage MGs, the proposed MGs reduce losses to gain significant enhancement in efficiency. It is widely believed that energy systems of the future will heavily depend on MGs rich in power electronics converters (PECs). This dissertation is focused on MGs with a high degree of self-sufficiency, without precluding sporadic links with the power grid. Potential applications of those MGs include: (a) distributed generation power systems, (b) ships, land vehicles, aircraft, and spacecraft, (c) users in need of power supply impervious to vulnerabilities of the grid, and (d) localities lacking an access to a grid.Modern pulse-width modulated PECs allow rapid and wide-range changes of voltages and currents. High switching frequencies result in high power quality and fast dynamic response, but each switching event causes energy loss related to the magnitudes of input voltage and output current. In the existing MGs, the bus voltages are maintained at a fixed level. However, many heavy loads, such as electric drives, operate most of the time with a reduced voltage, which is adjusted by decreasing the voltage gain of the feeding converter. This makes the voltage pulses high and narrow. If instead the pulses were made wide and low, then with the current unchanged the conduction losses would remain unchanged, but the switching losses would greatly decrease. This observation leads to the main idea of the dissertation, namely MGs whose dc-bus voltages are allowed to fluctuate and which are maintained at the lowest possible level. Loss minimization, apart from energy savings, may be critical for autonomous MGs with a tight balance of power.In this dissertation, two methods are proposed for calculating the minimum (optimum) required dc voltage level. In the first method, a central control unit allocates the minimum required dc voltages to individual buses by employing the information obtained from control systems of the adjustable voltage loads. For example, most of the variable-speed ac motors employ the so-called constant volts per hertz strategy, in which the relation between frequency and voltage is clearly specified. In the more sophisticated high-performance drives, the instantaneous values of the desired speed, torque, and current are available, allowing the required voltage estimation from the equation of power balance.In the second method, the problem of determining the optimal dc voltage and power settings is formulated as an optimization problem with the objective function of minimizing the converter losses. Genetic algorithm is utilized in solving the optimization problem. Due to limited available power from renewables, reducing the converter losses will enhance the survivability of the microgrid and ease the cooling requirements, resulting in a more compact system. A model of a 20-bus microgrid with the dc distribution network is employed to verify the effectiveness of the proposed methods