The demand for electricity of a particular location can vary significantly based on season, ambient temperature, time of the day etc. High demand can result in very high wholesale price of electricity. The reason for this is very short operating duration of peaking power plants which require large capital investments to establish. Those power plants remain idle for most of the time of a year except for some peak demand periods during hot summer days. This process is inherently inefficient but it is necessary to meet the uninterrupted power supply criterion. With the advantage of new technologies, demand response can be a preferable alternative, where peak reduction can be obtained during the short durations of peak demand by controlling loads. Some controllable loads are with thermal inertia and some loads are deferrable for a short duration without making any significant impact on users' lifestyle and comfort. Demand response can help to attain supply - demand balance without completely depending on expensive peaking power plants. In this research work, an incentive-based model is considered to determine the potential of peak demand reduction due to the participation of residential customers in a demand response program. Electric water heating and air-conditioning are two largest residential loads. In this work, hot water preheating and air-conditioning pre-cooling techniques are investigated with the help of developed mathematical models to find out demand response potentials of those loads. The developed water heater model is validated by comparing results of two test-case simulations with the expected outcomes. Additional energy loss possibility associated with water preheating is also investigated using the developed energy loss model. The preheating temperature set-point is mathematically determined to obtain maximum demand reduction by keeping thermal loss to a minimal level. Case studies are performed for 15 summer days to investigate the demand response potential of water preheating. Similarly, demand response potential associated with pre-cooling operation of air-conditioning is also investigated with the help of the developed mathematical model. The required temperature set-point modification is determined mathematically and validated with the help of known outdoor temperature profiles. Case studies are performed for 15 summer days to demonstrate effectiveness of this procedure. On the other hand, total load and demand response potential of a single house is usually too small to participate in an incentive-based demand response program. Thus, the scope of combining several houses together under a single platform is also investigated in this work. Monte Carlo procedure-based simulations are performed to get an insight about the best and the worst case demand response outcomes of a cluster of houses. In case of electrical water heater control, aggregate demand response potential of 25 houses is determined. Similarly, in case of air-conditioning control (pre-cooling), approximate values of maximum, minimum and mean demand reduction amounts are determined for a cluster of 25 houses. Expected increase in indoor temperature of a house is calculated. Afterwards, the air-conditioning demand scheduling algorithm is developed to keep aggregate air-conditioning power demand to a minimal level during a demand response event. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. / Master of Science

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