Over the last decade, supply-side constraints have resulted in widespread electricity shortage in Pakistan. At its peak, this amounted to over a 7 GW supply-demand gap and caused the electricity grid to be offline for vast swathes of population for many hours daily. Despite major supply-side investments acute shortages persist and a large percentage of relatively affluent households, estimated in millions, have countered this by investing in self-generation and battery storage technologies (usually lead-acid batteries because of their low cost). This paper summarizes the impact of this backup technology on the broader energy system in terms of efficiency losses for households and contribution to low-voltage grid congestion. Research findings suggest that the low efficiency of these backup systems has caused annual losses of around 3-4 TWh for the electric grid in Pakistan as well as overloading of transformers and frequent supply-demand imbalances. However, the mass adoption of these backup systems has also created an entire ecosystem which can enable massive demand side management and provide the framework for a future smart grid in Pakistan. Besides evaluating the opportunities, possible policy measures the government should undertake to enable this transition are also discussed.
Long-term electric power system planning models are frequently used to provide policy support in the context of the ongoing transition towards a low-carbon electric power system. In a liberalized market, this transition relies on generation company investment decisions. These decisions are shaped by both economic and behavioral factors. Agent-based modeling allows the incorporation of both factors in the description of the investment decision making process. Nevertheless, there are several challenges associated with the design of agent-based models such as the definition of the model structure and its lack of transparency. In this study, we aim to increase the transparency of investment decision making algorithms by shedding light on how implicit assumptions of the price projection methods used in these algorithms impact model results. To achieve this goal, we developed a core long-term agent-based model to assess different investment decision making algorithms from the literature and we introduced a novel price projection method based on optimization modeling. Our results show that investment decisions vary enormously depending on the assumptions and parameters used in investment decision making algorithms. We also found that our proposed price projection method is robust to parametric deviations. Thus, the proposed investment decision making algorithm enables agent-based modelers to mitigate the potential impacts of hidden implicit assumptions.
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