Environment dominated evaluation modeling and collocation optimization of a distributed energy system based on solar and biomass energy

The distributed energy system (DES) with multi-renewable energy complementary fits the current environmental policies and has the characteristics of energy conservation, emission reduction, and environment-friendly. In this work, the DES based on solar and biomass energy is established to meet the energy demand of 50 households in villages of cold areas. Considering the urgent environmental issue, a multi-dimensional evaluation system dominated by the environment is constructed. The maximization of carbon dioxide emission reduction rate, annual cost saving rate and primary energy saving rate are taken as the objective function. The parameters of the main equipment are as the decision variables, integrated with non-dominated sorted genetic algorithm-II, and the multi-objective optimization model is founded. The Entropy weight-TOPSIS method is used to solve the optimal capacity of the proposed system under the optimum operation mode. Under all operation modes, the optimized results are better than before optimization. In the mode of mixed operation, the comprehensive evaluation index reaches the highest value, increasing from 48.66% to 52.30%. And the comprehensive envi-ronmental index promoted from 75.90% to 82.53%, which is the best value among all schemes, verifying the reliability of the multi-objective optimization model and the emission reduction potential of the proposed system.

Automated summary

In this study, a distributed energy system based on solar and biomass energy was established to meet the energy demand of 50 households in cold areas. A multi-dimensional evaluation system dominated by the environment was constructed to address urgent environmental issues. Through the use of non-dominated sorted genetic algorithm-II and Entropy weight-TOPSIS method, the optimal capacity and operation mode of the proposed system were determined, leading to improved results compared to before optimization. This study validates the multi-objective optimization model and emission reduction potential of the proposed system.