Optimization of Spinning Reserve in Stand-alone Wind-Diesel Power Systems

Abstract

Spinning reserve carried on synchronized units is the most effective resource available to the system operators for managing unforeseen power unbalances, such as demand fluctuations and the sudden loss of generation equipment. The amount of reserve and the speed that it can effectively be deployed determine the supply reliability that the generation system can achieve. Carrying more spinning reserve reduces the probability that the generation system become unable to preserve the momentary power balance and costly remedial actions, such as involuntary load shedding, turns unavoidable to prevent a system collapse. Nevertheless, providing spinning reserve on a continuous basis is expensive. Indeed, the provision of spinning reserve entails incurring in startup costs to commit generating units in excess of the forecasted load, which consequently have to be dispatched at less efficient operating points. The problem of keeping the power balance is still more difficult in stand-alone wind-diesel power systems, since these systems are additionally subjected to random power fluctuations originated in the uncertain and intermittent nature of the wind resource. Furthermore, autonomous power system cannot rely on power imported from interconnections for preserving the power balance. The inherent characteristics of these systems require scheduling more reserve on synchronized units for ensuring adequate security and reliability levels. The higher reserve requirements may substantially deteriorate the economy of these supply systems. The costs of keeping spinning reserve must be compared with the benefits that it provides in terms of lower expected costs of interruptions. In essence, the optimal reserve level can be set so that the marginal cost of carrying an additional MW equals the marginal reduction of the expected load curtailment costs. Despite the apparent simplicity of this optimality condition, determining the optimal amount of spinning reserve in a practical setting presents substantial modelling complexities and computational challenges. Given the random nature of the disturbances and contingencies that may face a generation system, assessing the benefits of carrying a certain amount of spinning reserve involves quantifying the occurrence probability, Wind Power 2 duration, extent and costs of load loss events. Such evaluation entails modelling the stochastic behaviour of system operation by considering the random failure of system components and the stochastic fluctuations of load and wind generation. The problem is probabilistic in its very nature and thus it may be appropriately treated by applying stochastic modelling techniques. Only with the advent of more powerful computing hardware, the problem of optimizing the spinning reserve has attracted the interest of researchers and its solution is currently deemed practicable. This work proposes a novel method for determining the optimal amount of spinning reserve that should be carried in autonomous hybrid wind-diesel generation systems. The optimal spinning reserve is determined by comparing the cost of its provision with the economic benefits it delivers in terms of supply reliability. The proposed approach is still general and can be applied in straightforward manner to establish the optimal reserve level in large interconnected systems. The presented methodology considers with accuracy the probabilistic features of the load and the wind generation, as well as the random outages of the conventional generating units. By applying high-resolution chronological simulation techniques, the stochastic features of the integrated operation of the diesel units and the wind turbine can be detailed replicated. The mathematical model appropriately considers all relevant characteristics and operational constraints of the generating units, e.g. non-linear heat rate curve, maximum and minimum output, startup and synchronization time, minimum down and uptime, ramping, etc. Massive stochastic simulation methods allow assessing the system reliability and valuing the economic costs of loss load events. Global search methods like particle swarm optimization (PSO) are proposed for finding the optimal scheduling policy and spinning reserve requirement that minimizes the sum of the expected operation costs and the expected costs of the energy not served.