Optimal power flow program using particle swarm optimization | |
| Author | Jirawadee Polprasert |
| Call Number | AIT Diss. no.ET-15-03 |
| Subject(s) | Electric power transmission--Control Electric power systems--Computer programs Mathematical optimization--Computer programs |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Energy |
| Publisher | Asian Institute of Technology |
| Abstract | In this dissertation, the particle swarm optimization (PSO) methods are proposed for solving optimal generation dispatch problems including economic dispatch (ED) , optimal reactive power dispatch (ORPD) , security - constrained optimal power flow ( SCOPF), and optimal power flow (OPF) problems. Generally, the optimal generation dispatch is a nonlinear optimization problem which is used to minimize the total generation fuel cost in power system while satisfying the generator and network operating cons traints. However, the problems cannot be solved by conventional optimization techniques due to non - convex and non - differentiable objective functions and constraints. Thus, there are meta - heuristic and evolutional techniques that are employed for solving non - convex optimal generation dispatch problems. The objective of the economic dispatch (ED) problem is to determine the optimal active power generation outputs of each generating unit so as to minimize the total generation cost while satisfying power balance and generator operating constraints. In this dissertation, different ED problems are solved. Traditional ED problem is minimizing the total generation cost while satisfying generator and system operating constraints. For operation of boilers of thermal generating unit, the process of opening valve for multi - valve steam turbine produces the ripple curve in the heat rate curve of generator. This effect is considered in ED problem by adding rectified sinusoidal function in the quadratic objective function in ED with valve - point loading effects (ED - VPLE). In addition, the thermal generating units can be supplied by different types of fuel sources such as coal, oil, and natural gas. The fuel - cost characteristic is represented by several piecewise quadratic cost functions (PQCF) with multiple fuel sources in ED with Multiple Fuel Options (ED - MFO) . For solving these MFO and VPLE ED problems, the New Improved PSO (NIPSO) which is an improvement of PSO method by combining self - organizing hierarchical (SOH) and time - varying acceleration coefficients (TVAC) is proposed . The proposed method could overcome premature convergence during the early stages of the search to converge near global optimum solution. It has been tested and the obtained results are better than those from other methods in the literature in terms of total costs and computational times. For solving ORP D problem, an Improved Pseudo - Gradient Particle Swarm Optimization (IPG - PSO) method is proposed. The proposed method is improved by a dynamic weight fac tor using chaotic sequences and linearly decreasing inertia weighting factor which is used to diversify the search space during the early stage of iterations and intensify the search space during the later stage of iterations. Additionally, the IPG - PSO is guided by “pseudo - gradient” search to find a better direction of particles so that they can achieve a near global optimal solution. The proposed IPG - PSO method is applied to three different single - objective functions minimizing real power system loss, volt age deviation at load buses, and voltage stability index, satisfying power balance equations, generator voltages and reactive power limits, reactive power of shunt VAR capacitor compensation limits, transformer tap setting limits, voltages at load buses and transmission line loading limits. With these improvements, the proposed IPG - PSO method is more efficient and effective for solving ORPD resulting in a lower real power loss, smaller voltage deviation, and much improved voltage stability on the IEEE 30 - bus and IEEE 118 - bus systems than the other types of PSO algorithms and other meta - heuristic methods. Therefore, the proposed IPG - PSO method is potentially viable for online implementation due to consistent good results and fast computing time. This dissert ation proposes an improved pseudo - gradient search particle swarm optimization (IPG - PSO) for solving optimal power flow (OPF) with non - convex generator fuel cost functions. The objective of OPF problem is to minimize generator fuel cost considering valve point loading, voltage deviation and voltage stability index subject to power balance constraints and generator operating constraints, transformer tap setting constraints, shunt VAR compensator constraints, load bus voltage and line flow constraints. The pro posed IPG - PSO method is an improved PSO by linearly chaotic weight factor and guided by pseudo - gradient search for particle’s movement in an appropriate direction to escape the local minimum and better guide particles in the search space. Test results on t he IEEE 30 - bus and 118 - bus systems indicate that the proposed IPG - PSO method can obtain a higher solution quality than other methods, leading to generator fuel cost savings, voltage profile and voltage stability enhancements. For economic and secure opera tion in power system, a chaotic based particle swarm optimization with time - varying acceleration coefficients (CPSO - TVAC) is proposed for solving security constrained optimal power flow ( SC OPF) problem. The proposed CPSO - TVAC is an improved PSO mixing chao tic sequences and crossover operation to enhance the search ability to the global optimum solution. The proposed CPSO - TVAC based optimal power flow is used to minimize the total generation fuel cost while satisfying power balance constraints, real and reactive power generation limits, generator bus voltage limits ; tap setting transformer limits, and security constraints such as voltage and transmission line loading constraints. Test results on the IEEE 30 - bus and 118 - bus systems indicate that the proposed C PSO - TVAC method renders a lower total generation cost in a faster convergence rate than other heuristic methods, which is favorable for online implementation. n summary, the proposed PSO methods have been efficiently solving non - convex ED, ORPD, OPF , and SCOPF problems. Test results indicate that the proposed PSO solution methods are better than other methods reported in the literature in terms of less total cost and faster computational time. Therefore, the proposed methods are very favorable for optimal generation dispatch problems. Keywords: particle swarm optimization, self - organizing, chaotic sequence, stochastic weight trade - off, time - varying acceleration coefficients, time - varying inertia weight, economic dispatch, reactive power dispatch, optimal power flow, security - constrained optimal power flow, non - convex functions, pseudo - gradient search, improved pseudo - gradient, valve - point loading effect, multiple fuel options, voltage stability index, voltage deviation, meta - heuristic search |
| Year | 2015 |
| Type | Dissertation |
| School | School of Environment, Resources, and Development (SERD) |
| Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
| Academic Program/FoS | Energy Technology (ET) |
| Chairperson(s) | Weerakorn Ongsakul |
| Examination Committee(s) | Singh, Jai Govind ;Dailey, Matthew N. |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2015 |