The design and regulation of power equipments which installed in distribution system are based on single direction power flow. When distributed generators (DG) are added into distribution system, it may cause some technical problems such as two-way current, fault capacity and power quality. In general, the utility should make sure that its power system could be operated safely and reliably before integrating DG into the system. If there are no complete measurements for DG, the capacity of DG would be restricted by fault current, short circuit capacity, feeder voltage or other problems. In this research, the focus is on the influence of DG operations in distribution system and the increase of DG integration capacity. The impacts of different combinations of DG generation profiles and control strategies are first analyzed, followed by the use of particle swarm optimization (PSO) technique to search for better feeder reconfigurations in order to increase DG integration capacity.

In the next decade, electric vehicles (EV) will be heading to the road in a fast speed. Utility company would have no control over the future EV charging points or stations, and no direct control over periods and frequency of EV charging that could cause great effects to the existing distribution network operations if not well planned. Distribution system operation and expansion planning would become more complicated due to the high degree of uncertainty of the EV charging demand. Markov model is used in this study to calculate the probabilities and locations of EV charging. To mitigate the loading and voltage quality problem, feeder reconfiguration is proposed. The problem is formulated as an stochastic programming program with an objective function of minimizing total switching and system loss costs, and subject to radial structure of the distribution network and security constraints. The problem is solved by a binary particle swarm optimization technique. Test results indicate that feeder reconfiguration can be exercised to match loading patterns of different types of feeders (residential, commercial and industrial) with various stochastic charging scenarios, and consequently, reduce the impacts of EV charging and optimize the use of the existing network.

In this dissertation, the artificial intelligent Petri nets is applied to find the optimal switching operation for service restoration and feeder loading balance for 18-feeders distribution systems that containing the whole 24-hours load profiles of service zones. After the fault location has been identified and isolated for a system fault contingency, the Petri nets model with inference mechanism is derived and applied to solve the optimal load transfer among distribution feeders. For system normal operation condition, the load balancing among distribution feeders is obtained by the Petri nets model to enhance the operation efficiency of distribution systems. The switching operation, which will result in the loading balance among distribution feeders, is derived by the Petri nets model according to the loading cost of distribution systems. To determine the effectiveness of the proposed methodology, a Taipower (Taiwan Power Company) distribution system which serves a mixed types of customers is selected to perform the computer simulation. It is found that the Petri nets approach can enhance the solution process of fault restoration with proper load transfer and improve feeder load balance for distribution systems by considering the load characteristics of the service customers.

In a deregulated power market, customers would have more choices for their power service and the improvement of service quality has become a challenge to power transmission and distribution companies. Distribution system reliability that was traditionally considered within the planning activities, is now incorporated in the operational environment. This dissertation presents study results of a multi-objective feeder operation optimization problem that considers how to balance network efficiency, switching and reliability costs in a distribution network. The proposed method divides annual feeder load curve into multi periods of load levels and optimizes the feeder configurations for different load levels in annual operation planning. Customer load profiles and seasonal varying data of feeder section failure rates and customer interruption costs are considered. Simulations results demonstrate the time varying effects on the optimal distribution feeder reconfiguration and operation costs. A binary particle swarm optimization (BPSO) search is adopted to determine the feeder configuration in each time period. Test results indicate that not considering time varying effects and using only simplified fixed load and reliability parameters could underestimate the total loss to the utility and its customers.

As structure and size of electric power distribution systems are getting more complex, distribution automation schemes become more attractive. One of the features that is desirable in an automated system is feeder reconfiguration for loss reduction. Loss reduction can make considerable savings for a utility and results in released system capacity. There is also improved voltage regulation in the system as a result of reduced feeder voltage drop. In this thesis, multi branch exchange algorithm is introduced to solve the network reconfiguration for loss reduction problem. The proposed technique is based on heuristic techniques applied to constraint satisfaction optimization problems. A critical review of earlier methods related with feeder reconfiguration is presented. A computer program was developed using Matlab to simulate this algorithm and results of simulations demonstrate its advantages over single branch exchange method. Moreover, the results show that the final configuration is independent of the initial configuration and give assurance that any solution offered will have a radial configuration with all loads connected.

Power industry restructuring has created many opportunities for customers to reduce their electricity bills. In order to facilitate the retail choice in a competitive power market, the knowledge of hourly load shape by customer class is necessary. Requiring a meter as a prerequisite for lower voltage customers to choose a power supplier is not considered practical at the present time. In order to be used by Energy Service Provider (ESP) to assign customers to specific load profiles with certainty factors, a technique which bases on load research and customers¡¦ monthly energy usage data for a preliminary screening of customer load profiles is required. Distribution systems supply electricity to different mixtures of customers, due to lack of field measurements, load point data used in distribution network studies have various degrees of uncertainties. In order to take the expected uncertainties in the demand into account, many previous methods have used fuzzy load models in their studies. However, the issue of deriving these models has not been discussed. To address this issue, an approach for building these fuzzy load models is needed. Load aggregation allows customers to purchase electricity at a lower price. In some contracts, load factor is considered as one critical aspect of aggregation. To facilitate a better load aggregation in distribution networks, feeder reconfiguration could be used to improve the load factor in a distribution subsystem. To solve the aforementioned problems, two data mining techniques, namely, the fuzzy c-means (FCM) method and an Artificial Neural Network (ANN) based pattern recognition technique, are proposed for load profiling and customer class assignment. A variant to the previous load profiling technique, customer hourly load distributions obtained from load research can be converted to fuzzy membership functions based on a possibility¡Vprobability consistency principle. With the customer class fuzzy load profiles, customer monthly power consumption and feeder load measurements, hourly loads of each distribution transformer on the feeder can be estimated and used in distribution network analysis. After feeder models are established, feeder reconfiguration based on binary particle swarm optimization (BPSO) technique is used to improve feeder load factors. Test results based on several simple sample networks have shown that the proposed feeder reconfiguration method could improve customers¡¦ position for a good bargain in electricity service.