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Large-scale integration of wind power is one of the main challenges in today's power systems. Therefore, there is an increased importance to generate more accurate yet simplified wind farm (WF) models. The current modelling techniques of WFs depend on aggregationto reduce the degree of model complexity and computational time. The two techniques of aggregation are adopted in the literature: fully aggregated model (FAM) and multi-machine model where the WF is divided into a number of aggregated models for different sections of the WF. A small-signal model based comparison between the two techniques of aggregation is introduced in this study. The aim is to investigate the impacts of the full aggregation of the wind turbines on the overall system critical modes under weak grid conditions as compared to the multi-machine modelling approach. The comparison is done for different operating conditions (output active and reactive power) of the WF and also under different short-circuit ratios to reveal to what extent the FAM can be relied on to provide an accurate response of large WFs.

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Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.