Abstract
Computational time in optimization models scales with the number of time steps. To save time, solver time resolution can be reduced and input data can be down-sampled into representative periods such as one or a few representative days per month. However, such data reduction can come at the expense of solution accuracy. In this work, the impact of reduction of input data is systematically isolated considering an optimization which solves an energy system using representative days. A new data reduction method aggregates annual hourly demand data into representative days which preserve demand peaks in the original profiles. The proposed data reduction approach is tested on a real energy system and real annual hourly demand data where the system is optimized to minimize total annual costs. Compared to the full-resolution optimization of the energy system, the total annual energy cost error is found to be equal or less than 0.22% when peaks in customer demand are preserved. Errors are significantly larger for reduction methods that do not preserve peak demand. Solar photovoltaic data reduction effects are also analyzed. This paper demonstrates a need for data reduction methods which consider demand peaks explicitly.
Highlights
Microgrid planning can reduce investment and operational costs, through reducing demand and energy charges or increasing revenue for energy services provided to the utility.1 Optimization models can determine a cost-optimal solution without the need to iterate through a manually defined search space of solutions
This paper demonstrates a need for data reduction methods which consider demand peaks explicitly
The error is a consequence of the peak day calculations, where demand peaks are subtracted from weekend and weekday data sets to construct peak profiles on an hour-by-hour basis, but total annual consumption is calculated by multiplying Rm;dt;h by a single value nm;dt per profile
Summary
Microgrid planning can reduce investment and operational costs, through reducing demand and energy charges or increasing revenue for energy services provided to the utility. Optimization models can determine a cost-optimal solution without the need to iterate through a manually defined search space of solutions. Reducing the computational time required to solve optimization problems can be at odds with the need for accurate solutions. Time series input data such as demand, solar insolation, and utility rates vary seasonally, daily, and hourly, and commercial utility rates often include demand charges, which can make up a disproportionate amount of annual electric charges. As interactions between fluctuating electricity prices, demand charges, and distributed energy resource (DER) dispatch significantly impact microgrid design, accurate modeling requires demand, solar insolation, and utility data to be represented with a high level of granularity, including demand peaks. A single run of optimization models using an annual horizon and hourly granularity for time series data can take multiple days, a problem further exacerbated when considering multiple years or topology sizing
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