Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy

The accurate prediction of optimum load is crucial for electric utilities while planning and forecasting. Errors in the planning stage, regardless of their nature may result in operational loss of utility. The complex interplay of natural events, man-made factors, and global policies make forecasting future power demand incredibly challenging, thus making prediction models to struggle with accurate predictions. Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. Demand forecasting becomes even more difficult in polygeneration utilities with renewable energy sources integrated to meet the varying demands. This research work aims estimation of demand and line losses in utilities with generation deficiencies that implement demand side management. A polygeneration system integrating renewable energy system (RES) is dynamically tested under various electrical loading conditions. The generation comprises solar PV and wind turbine, each of 1 MW along with 1.5 MW auxiliary diesel engines is simulated in TRNSYS (R) to meet the varying demand under diverse weather conditions. Results show that RES can supply 77 % of energy demand, whereas 23 % of the load is met by fossil fuel (diesel). The wind turbine shows consistent performance and can replace traditional fossil fuel-based electricity generation system as a baseline supply. The polygeneration system converts surplus renewable energy into 5,000-7,000 kg of hydrogen for efficient storage and future electricity use, supporting sustainable energy needs.