For the power market participators, there are competition and more trade opportunities in the power industry under the deregulation. In the electricity market, the bidding model is adopted instead of the cost model. GenCos try to maximize the profit under bidding model according to the power demand. Electricity becomes commodity and its price varies with power demand, bidding strategy and the grid. GenCos perform the unit commitment in a price volatile environment to reach the maximal profit. In a deregulation environment, Independent System Operator (ISO) is very often responsible for the electricity auction and secured power scheduling. The ISO operation may involve all kinds of risks. These risks include price volatility risk, bidding risk, congestion risk, and so on. For some markets, it is very important how GenCos determine the optimal unit commitment schedule considering risk management. A good risk analysis will help GenCo maximize profit and purse sustainable development. In this study, price forecasting is developed to provide information for power producers to develop bidding strategies to maximize profit. Profit-Based Unit Commitment (PBUC) model was also derived. An Enhanced Immune Algorithm (EIA) is developed to solve the PBUC problem. Finally, the Value-at-Risk (VAR) of GenCos is found with a present confident level. Simulation results provide a risk management rule to find an optimal risk control strategy to maximize profit and raise its compatibility against other players.

The objective of this thesis is to enhance the reliability analysis of Relaying systems and build-up model by Markov theory for taipower transmission lines. The set of combinatory multi-elements can be expressed a transition matrix for any pilot protection analysis. The protective reliability system need for transmission protection is introduced and the block modeling consists of protective relays, communication set and circuit breaker. The block modeling is applied for the analysis of the reliability and availability of protection systems by Markov theory, which can be need to derive the adapative maintance cycle by Markov reliability modeling. The system reliability is analysis related to the interruption of supply power. There many methods to be used for the analysis of system reliability such as state space, network. etc. The Markov modeling is more complicated and difficult, however better time-vary probability functions can be defined, for stochastic modeling, the system reliability at any time axis can be obtained by Markov transition matrix, with the time-vary Markov transition matrix. The customers served by each substation can be affected according to the states of transmission lines healthy. Althouth 80% of system faults occurs in the distribution system, transmission line faults will cause more serious service outage. According to the Kauo-Ping transmission line model in taipower, the optimal protection relay planning is solved by minimizing the overall outage cost of customer service interruption and investment protection relay equipments for transmission power systems with immune algorithm. The objective function and constraints are expressed as antigen, and all feasible solutions are expressed as antibody. The diversity of antibody is then enhanced by proximity of antigen so that the global optimization during the solution process can be obtained. It is found that the power service can be restored effectively with the optimal planning of protection relay by the proposed immune algorithm. Based on the computer simulation of protection relay planning, different protection relaying strategies optimal relay planning and customer loss, can be considered for different to enhance the reliability of protection relay system for loss interruption of customer power outage.

The objective of this thesis is to enhance the efficiency of Mass Rapid Transit (MRT) system and improve the power quality by reducing harmonic distortion. The energy consumption of an MRT system by considering the annual ridership and the stochastic operation characteristics of train sets are used to find the optimal placement of traction substations to enhance the operation efficiency of MRT systems. To mitigate the harmonic distortion, the installation location and capacity of harmonic filters are designed and verified by computer simulation. The software programs for AC/DC load flow study and harmonic distortion analysis have been developed and integrated to perform power system simulation of MRT operation. The mathematical model of 12-pulse uncontrolled rectifiers without interphase transformers is derived and implemented in the programs to obtain more accurate simulation results. The optimal inverter substation planning is solved by minimizing the overall cost of power consumption and inverter investment for mass rapid transit power systems with immune algorithm. The objective function and constraints are expressed as antigen, and all feasible solutions are expressed as antibody. The diversity of antibody is then enhanced by proximity of antigen so that the global optimization during the solution process can be obtained. It is found that the energy regeneration can be restored effectively with the optimal planning of inverters by the proposed immune algorithm. Based on the computer simulation of Taipei MRT system, the voltage harmonic distortion is varied dramatically with the dynamic load behavior of train sets. The stochastic harmonic load flow analysis is performed to investigate the power quality problem for an electrified rapid transit system. Different strategies of harmonic distortion mitigation have been proposed by minimizing the objective function to solve the optimal sizes and locations of harmonic filters so that the harmonic distortion can be reduced and reactive power compensation can be obtained at the same time. By performing the immune algorithm, the harmonic filters with proper capacity and the corresponding switching time for filter commitment are determined. It is found that the harmonic distortion can be effectively reduced for the MRT system by the proper design of harmonic filters.

Mass Rapid Transit(MRT) plays a very important role for the city development,the investment cost is very expensive. It is necessary to derive the MRT system planning by considering the service reliability and performance index according to the forecast of annual ridership. With the less ridership as compared to Taipei MRT network, Kaohsiung MRT has to be developed to achieve the most cost effective investment of power supply and rolling stock planning. This thesis is to investigate the proper expansion planning of traction substations (TSS) for an electrified mass rapid transit system. The motion equation of train sets is used to solve the mechanical power consumption at each time snapshot according to the operation timetable, the passenger ridership and various types of operation resistance. The mathematical models of power converters in traction substations for different operation modes have been derived. With all train sets operated along the main line, the AC/DC load flow analysis is performed to find power demand of all traction substations for annual system peak operation over the study period. The objective function is formulated by considering both the voltage drop of train sets and investment cost of traction substations as the equivalent cost of all feasible states of each year. By performing the dynamic programming (DP) and immune algorithm (IA), the expansion planning of traction substations to achieve the minimum overall cost has been solved by identifying the optimal capacity and locations of new traction substations to be committed at each year.

With the rapid growth of load demand, the distribution system is becoming very complicated such that the operation efficiency and service quality are deteriorated during recent years. Engineers have to solve the problems by applying new technologies to enhance the efficiency of distribution system. In this thesis, an immune algorithm(IA) based on weighting selection as a decision maker is proposed to reach the desired switching operations such that transformer and feeder loading balance can be achieved. The IA antigen and antibody are equivalent to the objective and the feasible solution for a conventional optimization method. The concept of the information entropy is also introduced as a measure of diversity for the population to avoid falling into a local optimal solution. This algorithm prevents the possibility of stagnation in the iteration process and achieves the fast convergence for the global optimization. With the object-orient programming(OOP), this research project is to create the relationship of distribution element objects and encapsulation of data with all 22KV underground systems in Taichung district. The OOP does provide an effective tool for the management of distribution system database and the fault detection, isolation, and service restoration(FDIR) function of feeders and main transformers. According to the attributes of line switches, we can create the 22KV distribution system configuration with the topology processor. In order to calculate the current flows of line switches, this project will also execute the three phase load flow program with the customer information system(CIS), load survey, outage management information system(OMIS), and the data of all feeders and main transformers. In this thesis, the IA is used to solve the optimal switching problem by considering the customer load characteristics for the normal operation and the overload contingency of the distribution system. The efficiency of immune algorithm to solve the problem is verified by comparing to the computing time of the conventional binary integer programming for decision making of switching operation. A Taichung district distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed methodology for solving the optimal switching operation of distribution system. The result of this thesis will be an important reference for distribution automation in Taiwan.

The thesis investigates the optimal expansion planning of substations for the distribution system of Taipei City District of Taiwan Power Company. The small area load forecasting is executed with the support of Outage Management System(OMS) database. The capacity expansion of distribution substations is obtained by considering the annual load growth of each service area to achieve the cost effectiveness of substation investment. The geographic information of each service zone has been retrieved form the OMS data. With the land use planning of Taipei City Government, the load density of each small area for the target year is derived according to the final floor area and development strength of the land base. The load forecasting of each small area is then solved by considering the load growth of each customer class, which is then used for the expansion planning of substations. After determining the small area load forecasting for the final target year, the center of gravity method is applied to find the geographic blocks of all substations and the corresponding service areas at the target year. The power loading of each small area is used to calculate the power loading loss of which service area to solve the optimal location within the block for each substation. Based on the annual load forecasting of all small areas, the expansion planning of distribution substations for Taipei City District is derived by Dynamic Programming(DP) and Immune Algorithm(IA) to achieve minimization of power loading loss with subject to the operation constraint. By the proposed methodology, the unit commitment of distribution substations is determined to meet the load growth of service area and achieve power loading loss minimization of distribution systems.