The optimization of economic dispatch in hybrid diesel photovoltaic systems within Non-Interconnected Zones (NIZ) is essential to enhance energy sustainability and reduce operating costs. The variability of renewable generation and the uncertainty of electricity demand hinder efficient planning, underscoring the need for advanced optimization models. The purpose of this research was to develop an economic dispatch model for diesel generators integrated with photovoltaic generation, incorporating electricity demand forecasting. The methodology was based on formulating a quadratic programming problem and applying vector autoregressive models supported by socioeconomic variables. Simulations were carried out in Python using the IPOPT (Interior Point Optimizer) solver. The proposed model aimed to optimize operational efficiency by reducing CO₂ emissions and production costs. The analysis was applied to a modified version of the IEEE 33-bus distribution system. The results showed that the optimal dispatch reduced generation costs by 32.1%, decreasing from USD 15 853.83 in the base scenario to USD 10 769.82 with the inclusion of photovoltaic generation. Likewise, daily fuel consumption decreased by 4 227.4 gallons, while CO₂ emissions were reduced by 41 926.1 kg. In addition, solar generation contributed 4 249.2 kWh per day, equivalent to 5.09% of total demand, directly reducing technical losses from 292 kW to 243 kW. In conclusion, the results demonstrate that the integration of predictive models and optimization techniques improves operational performance and supports sustainable energy planning in isolated communities.
Carlos Arturo Páez Chica (Thu,) studied this question.