Geographical averaging and ancillary services for stochastic power generation

The distribution of stochastic generation from renewables across different geographical locations can, in certain cases, help to mitigate the inherent variability in output. This variability of generation from renewables may (1) increase the operating costs of the conventional generators used to follow the net load not supplied by stochastic capacity and (2) increase the amount of reserve conventional generating capacity needed to maintain Operating Reliability. In this scenario, customers have lower wholesale prices, due to reductions in the total annual generation from fossil fuels, while generators face higher operating costs for conventional generators caused by additional ramping that partly offset the customer benefits. However, the lower wholesale prices ($/MWh) imply lower annual earnings for conventional generators that lead to higher amounts of missing money ($/MW) needed to maintain the financial adequacy of installed generating units. The objective of this paper is to determine how variability from a stochastic generation resource affects the optimal hour-to-hour dispatch of generating units and the corresponding operating costs and wholesale prices. The results show that the inclusion of ramping costs for conventional generation affect the amount of energy dispatched from the stochastic generator, and the total costs composition observed in the system. The Cornell SuperOPF is used to illustrate how the operating costs and wholesale prices can be determined for a reliable network (the amount of conventional generating capacity needed to maintain Operating Reliability is determined endogenously). The results in this paper use a typical daily pattern of load and capture the cost of ramping by including additions to the operating costs of the generating units associated with the hour-to-hour changes in their optimal dispatch. The calculations for determining endogenous up and down reserves are included, and the wind generation cost is assumed to be zero. Ad- - ditionally, the maximum and minimum available capacities for all hours in the day are constrained to the optimal capacities for the hours with the highest and the lowest loads. Different scenarios are evaluated for a given hourly realization of wind speeds using specified amounts of installed wind capacity with and without ramping costs. The analysis also evaluates the effects of eliminating network constraints, as well as the elimination of wind variability by accounting for the effects of spatial aggregation of different wind locations.