Locational Capacity Credit Evaluation

Boosted by U.K. governmental targets of securing 10% of electricity generation from renewable resources by 2010 and 20% by 2020 and widespread public support for renewable energy, distributed generators (DGs) are rapidly increasing in electrical power systems. Among the renewable DGs, wind energy is emerging as the popular choice due to its mature technology and low operation and maintenance costs. This paper utilizes the reliability aspects of electrical power systems to provide a probabilistic approach to determine the capacity credit (CC) of distributed generators. Monte Carlo simulations are employed to cater for the stochastic nature of the simulations and each trial is validated using the Newton-Raphson optimal load flow solution. Bernoulli trials are used to simulate the availability of network components. An algorithm to evaluate the capacity credit due to distributed generation (DG) connected in the network is shown. Hence, the amount of conventional generation which can be backed off from the bulk supply point (BSP) of the distribution network can be quantified. Wind energy production is known to depend on the wind regime experienced by the wind turbines, as well as the geographical landscape and the presence of wind obstacles, which in turn determines capacity credit. Having verified that adding a specific DG at each node does not violate any operating constraints, it was assumed that thermal constraints would not affect CC in this particular system. In a more in-depth study, it is shown in this paper that capacity credit also varies with the different voltage levels at which the wind turbine generators are connected and the loading level of the distribution network. Moreover, it is shown that CC does not vary with the base case reliability, but rather with wind penetration levels.