plans outlined in the 7th Five Year Plan but still imply a substantial expansion of renewable generation capacity over the coming years.

The 10% target implies a substantial increase in renewable energy generation. Given the recent growth rates of 8% per annum in electricity demand, we expect that power generation will need to grow to 73.5 TWh by 2020 of which renewables will have to account for 7.3 TWh to meet the 10% target. Achieving the 7.3 TWh target, will require the installation of approximately 5 GW of variable renewable generation capacity. The Scaling Up Renewable Energy Program (SREP) study conducted in late 2015 concluded the long term potential of solar covering all technologies to be around 3.7 GW and that for wind to be 624 MW (excluding flood prone areas but allowing for low quality wind sites that yields capacity factor as low as 20%).<sup>7</sup> Given that the cost of solar in particularly has significantly improved since then, we have considered the possibility of a higher volume of 4.2 GW of solar and 0.5 GW of wind to be potentially built by 2025, consistent with the 10% target. We have also explored factors that would determine economic merit of these projects as part of the least‐cost analysis.

# Methodology

The centerpiece of the analytical framework that we use is a least‐cost optimization model to determine the investment and dispatch plan over 2017‐2025. Using this model, we identify the best portfolio of generation (including renewable generation) and associated dispatch to meet future demand growth in power. Demand growth is exogenous to modeling and the projection we have used relies on recent load growth in Bangladesh. The inputs to the model comprise economic data concerning the existing generation fleet, planned capacity expansions, and fuel prices along with solar and wind resource data, and key financing assumptions.

There are three major components of the analytical model that are performed in sequence:

1. A **long‐term investment optimization** that uses a mixed integer programming (MIP) model to optimize investment decisions and performs a high‐level dispatch analysis around a load duration curve that represents demand for each year using 30 load conditions from peak to off‐peak for each of the years 2017‐2025. The long‐term model also considers some of the operational features including the spinning reserve allocation among generators for each load condition that may often be critical because of limited availability of it exacerbated by the need to manage variability of solar (and wind);

2. A **short‐term dispatch optimization model** that uses a linear programming model to minimize the short‐term cost of supply given a capacity plan. Dispatch planning differs from investment planning in that dispatch optimizes plant utilization within a select, short timeframe. We model dispatch over a representative four‐week period in 2017, the base year, and 2025 the last year of the forecast period. Dispatch modeling takes the results from the investment planning model and optimizes generation based on marginal costs of each generator. Hence, dispatch modeling ignores fixed costs or investment costs. The result is a generation scenario where the power output from plants with the lowest marginal cost is maximized to meet demand over the select period. In the investment planning

> 7 Sustainable Renewable Energy and Development Authority (SREDA), _Scaling Up Renewable Energy in Low Income Countries (SREP)_ , Ministry of Power Energy and Mineral Resource, October 2015.

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