In recent years, the awareness of environmental protection has made the power dispatch model no longer purely economical-oriented. This thesis proposed the application of particle swarm optimization (PSO) algorithm and interactive compromise programming method to solve the 24-hour two-objective power dispatch problem. Considering simultaneously the lowest generating cost and the lowest pollution emission, the two mutually-conflicting objectives will choose a compromised dispatch model. This thesis joined the mixed-integer programming problem of optimal power flow (MIOPF) with the dynamic economic dispatch (DED), making this dispatch solution more realistic without electrical violations; The MIOPF considers both continuous and discrete types of variables. The continuous variables are the generating unit real power output and the generator-bus voltage magnitudes; the discrete variables are the shunt capacitor banks and transformer tap setting. Simulation were run on the standard IEEE 30 Bus system. In order to avoid the PSO local optimality problem, this thesis proposed the utilization of the PSO algorithm with time-varying acceleration coefficients (PSO_TVAC) plus the local random search method (LRS), so it can quickly and effectively reach the optimal solution, without advantages of performance and accuracy of PSO. This thesis also proposed the consideration of the available transfer capability (ATC) on transmission lines of the existing dispatch model. Applying sensitivity factors to calculate each generator¡¦s available transfer capability that can be offered in the analyzed time interval, enables the creation of a new constraint. Joined with the dynamic economic dispatch problem, it will make possible that a load client wishes to raise its demand. Simultaneously taking care of the minimum cost and the limits of system security, better dispatch results could be expected.

Carbon dioxide (CO2) is the most important component of Greenhouse Gas (GHG) that causes global warming and sea-level rising. Thermal power plants dominate electric power generation in the world, and has been reported to be the major contributor of CO2 emission. To prevent the related global warming caused by GHG emission, carbon quota trading is implemented and becomes a gradually arising market. This thesis proposed a research focused on the relationship between the carbon trading scheme and dynamic economic dispatch (DED) problem for the public utility. A model of the carbon trading market was investigated and introduced into DED problem incorporating wind and solar power plant. A refined particle swarm optimization (PSO) algorithm, PSO with time-varying acceleration coefficients (PSO-TVAC), is applied to determine the DED strategy with the incorporation of independent power providers (IPPs) and green power plant. The model of the carbon trading was considered in the DED problem. Carbon reduction is treated as the inner-cost of utility, and the fictitious carbon quotas can be resold to the market, while the energy shortage can be satisfied by purchasing quotas from the market. In order to avoid premature convergence of the original PSO, the PSO-TVAC method is introduced to improve the searching efficiency.

In the recent years there has been a great deal of attention on the optimal demand and supply side strategy. The increase in renewable energy sources and the expansion in demand response programmes has shown the need for a robust power system. These changes in power system require the control of the uncertain generation and load at the same time. Therefore, it is important to provide an optimal scheduling strategy that can meet an adequate energy mix under demand response without affecting the system reliability and economic performance. This thesis addresses the following four aspects to these changes. First, a renewable obligation model is proposed to maintain an adequate energy mix in the economic dispatch model while minimising the operational costs of the allocated spinning reserves. This method considers a minimum renewable penetration that must be achieved daily in the energy mix. If the renewable quota is not achieved, the generation companies are penalised by the system operator. The uncertainty of renewable energy sources are modelled using the probability density functions and these functions are used for scheduling output power from these generators. The overall problem is formulated as a security constrained economic dispatch problem. Second, a combined economic and demand response optimisation model under a renewable obligation is presented. Real data from a large-scale demand response programme are used in the model. The model finds an optimal power dispatch strategy which takes advantage of demand response to minimise generation cost and maximise renewable penetration. The optimisation model is applied to a South African large-scale demand response programme in which the system operator can directly control the participation of the electrical water heaters at a substation level. Actual load profile before and after demand reduction are used to assist the system operator in making optimal decisions on whether a substation should participate in the demand response programme. The application of these real demand response data avoids traditional approaches which assume arbitrary controllability of flexible loads. Third, a stochastic multi-objective economic dispatch model is presented under a renewable obligation. This approach minimises the total operating costs of generators and spinning reserves under renewable obligation while maximising renewable penetration. The intermittency nature of the renewable energy sources is modelled using dynamic scenarios and the proposed model shows the effectiveness of the renewable obligation policy framework. Due to the computational complexity of all possible scenarios, a scenario reduction method is applied to reduce the number of scenarios and solve the model. A Pareto optimal solution is presented for a renewable obligation and further decision making is conducted to assess the trade-offs associated with the Pareto front. Four, a combined risk constrained stochastic economic dispatch and demand response model is presented under renewable obligation. An incentive based optimal power dispatch strategy is implemented to minimise generation costs and maximise renewable penetration. In addition, a risk-constrained approach is used to control the financial risks of the generation company under demand response programme. The coordination strategy for the generation companies to dispatch power using thermal generators and renewable energy sources while maintaining an adequate spinning reserve is presented. The proposed model is robust and can achieve significant demand reduction while increasing renewable penetration and decreasing the financial risks for generation companies. / Thesis (PhD (Electrical Engineering))--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / PhD (Electrical Engineering) / Unrestricted