The optimal placement of phasor measurement units and their effects on state estimation

Phasor measurement units (PMUs) are a key new technology for use in electric power systems as a backbone for sensing and measurement and to improve interface and decision support using instantaneous PMU data which will drive faster simulations and advanced visualisation tools that will help system operators assess dynamic challenges to system stability. The two main objectives of this work are to investigate and develop 1. a method for the algorithmic placement of a minimum number of PMUs into a system to ensure full observability, 2. conventional, hybrid and linear state estimation techniques to incorporate and utilize PMU measurement data to perform state estimation and to study the effects that differing PMU placement positions have on the accuracy of the resultant state estimator solution

MULTI-OBJECTIVE OPTIMAL CAPACITY AND PLACEMENT OF DISTRIBUTED GENERATORS IN THE POWER SYSTEM NETWORKS USING ATOM SEARCH OPTIMIZATION METHOD

Nowadays, renewable energy sources become a significant source of energy in the new millennium. The continuous penetration of dispersed resources of the reactive power into power systems is predicted to introduce new challenges. Power loss mitigation and voltage profile development are the major investigation challenges that recently attracted the attention of researchers in the field of power systems. Distributed generation (DG) is widely preferred because it is a highly effective solution that strengthens the performance of power system networks. This multiobjective function study aims to minimise power losses in the feeders, sustain voltage levels and reduce the application cost of DGs by adapting the atom search optimisation simulated on MATLAB software. Two different IEEE power test systems, namely, a 33 bus radial distribution system (RDS) and a 14-bus power system that hosts 1, 2 and 3 DGs in both systems, are demonstrated in this research. Correspondingly, backward–forward sweep and Newton–Raphson power flow methods are used for each system. The proposed technique is compared with the genetic algorithm particle swarm optimisation (GA-PSO) method. Results depict the effectiveness of the proposed method in minimising system power losses and in regulating the voltage profile where the power loss reduction is 25.38% in the 33 bus RDS using 2 DGs. By contrast, the power loss reduction percentages in the 14 bus system are 0.316% and 0.169% in systems with 1 and 2 DGs, respectively. The voltage profile has been enhanced compared with those in the original case and the results obtained from the GA-PSO method

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

DC Distribution with Fuel Cells as Distributed Energy Resources

In recent years, the idea of a direct current (DC) distribution systems has returned to the public eye as a result of DC based energy sources. The battle between alternating current (AC) and DC began with Thomas Edison and George Westinghouse long ago with AC becoming the victor as a result of the transformer. However, development of power electronics and switching devices and DC based energy sources such as fuel cells has forced the reevaluation of DC systems. This dissertation is focused on once again addressing this topic, but with an examination of the technologies as they stand today. Along with examining basic efficiency of the competing technologies, distributed generation has as well peaked the interest and will be incorporated into the analysis. Optimum placement of this distributed generation (DG) source is vital to obtain the maximum benefit and techniques for optimization are examined. The sensitivity and “rules of thumb” for placement of DG in a DC system are developed. Last, the largest driving factor for system changes is cost. A brief examination of the economic factors that have the potential to drive DC systems is examined

Integration of Energy Storage into a Future Energy System with a High Penetration of Distributed Photovoltaic Generation

Energy storage units (ESU) are increasingly used in electrical distribution systems because they can perform many functions compared with traditional equipment. These include peak shaving, voltage regulation, frequency regulation, provision of spinning reserve, and aiding integration of renewable generation by mitigating the effects of intermittency. As is the case with other equipment on electric distribution systems, it is necessary to follow appropriate methodologies in order to ensure that ESU are installed in a cost-effective manner and their benefits are realized. However, the necessary methodologies for integration of ESU have not kept pace with developments in both ESU and distribution systems. This work develops methodologies to integrate ESU into distribution systems by selecting the necessary storage technologies, energy capacities, power ratings, converter topologies, control strategies, and design lifetimes of ESU. In doing so, the impact of new technologies and issues such as volt-VAR optimization (VVO), intermittency of photovoltaic (PV) inverters, and the smart PV inverter proposed by EPRI are considered. The salient contributions of this dissertation follow. A unified methodology is developed for storage technology selection, storage capacity selection, and scheduling of an ESU used for energy arbitrage. The methodology is applied to make technology recommendations and to reveal that there exists a cost-optimal design lifetime for such an ESU. A methodology is developed for capacity selection of an ESU providing both energy arbitrage and ancillary services under a stochastic pricing structure. The ESU designed is evaluated using ridge regression for price forecasting; Ridge regression applied to overcome numerical stability and overfitting issues associated with the large number of highly correlated predictors. Heuristics are developed to speed convergence of simulated annealing for placement of distributed ESU. Scaling and clustering methods are also applied to reduce computation time for placement of ESU (or any other shunt-connected device) on a distribution system. A probabilistic model for cloud-induced photovoltaic (PV) intermittency of a single PV installation is developed and applied to the design of ESU

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Modeling of Utility Distribution Feeder in OpenDSS with Steady State Impact Analysis of Distributed Generation

With the deregulation of the electric power industry and the advancement of new technologies, the attention of the utilities has been drawn towards adopting Distributed Generation (DG) into their existing infrastructure. The deployment of DG brings ample technological and environmental benefits to the traditional distribution networks. The appropriate sizing and placement of DGs which generate power locally to fulfill consumer demands, helps to reduce power losses and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a utility distribution feeder in OpenDSS. Studies are conducted on the data obtained from American Electric Power utility. This thesis develops models for 12.47 kV (medium voltage) distribution feeders in OpenDSS by utilizing the existing models in CYMDIST. The model conversion is achieved by a detailed one-to-one component matching approach for multi phased lines, conductors, underground cables, loads, regulators and capacitor banks. The power flow results of OpenDSS and CYMDIST are compared to derive important conclusions.;The second major objective is to analyze the impacts of DG on distribution systems and two focus areas are chosen, namely: effect on voltage profiles and losses of the system and the effects on power market operation. To analyze the impacts of DG on the distribution systems, Photovoltaic (PV) system with varying penetration levels are integrated at different locations along the developed feeder model. PV systems are one of the fastest growing DG technologies, with a lot of utilities in North America expressing interest in its implementation. Many utilities either receive incentives or are mandated by green-generation portfolio regulations to install solar PV systems on their feeders. The large number of PV interconnection requests to the utilities has led to typical studies in the areas of power quality, protection and operation of distribution feeders. The high penetration of PV into the system throws up some interesting implications for the utilities. Bidirectional power flow into a distribution system, (which is designed for one way power flow) may impact system voltage profiles and losses. In this thesis, the effects of voltage unbalance and the losses of the feeder are analyzed for different PV location and penetration scenarios.;Further, this thesis tries to assess the impact of DG on power market operations. In a deregulated competitive market, Generation companies (Genco) sell electricity to the Power exchange (PX) from which large customers such as distribution companies (Disco) and aggregators may purchase electricity to meet their needs through a double sided bidding system. The reliable and efficient operation of this new market structure is ensured by an independent body known as the Independent System Operator (ISO). Under such a market structure, a particular type of unit commitment, called the Price Based Unit Commitment (PBUC) is used by the Genco to determine optimal bids in order to maximize its profit. However, the inclusion of intermittent DG resources such as wind farms by the Gencos causes uncertainty in PBUC schedules. In this research, the effects of intermittency in the DG resource availability on the PBUC schedule of a Genco owning a distribution side wind farm are analyzed

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources