A novel socio-economic-environmental model to maximize prosumer satisfaction in smart residential complexes.

As the adoption of distributed and renewable energy resources helps consumers evolve into prosumers who generate and trade their own energy, optimizing distributed renewable energy resources, electric vehicles, and energy storage in smart residential complexes is crucial to enhance satisfaction and capitalize on their potential for environmental and economic benefits. Nonetheless, limited progress has been made to quantify the impact of prosumer and energy manager preferences and the integrated socio-economic-environmental dynamics on energy scheduling and stakeholder satisfaction within smart communities. This study aims to overcome this challenge by proposing a socio-economic-environmental optimization model based on a preference-oriented management approach, which quantifies the impact of energy scheduling on stakeholder satisfaction in smart communities. The model inputs include infrastructure configurations, energy consumption patterns, and weather data. The simulation method includes the application of deep learning and System Advisor Model software in predicting weather conditions and photovoltaic and wind turbine systems energy generation. The simulation results are then imported as parameters in GAMS software to optimize the stakeholder satisfaction using CPLEX solver. Quantitative results show that with the simultaneous use of electric vehicles and energy exchange between energy communities, the highest level of satisfaction of the stakeholders in the smart grid including energy communities has been achieved at hour 24 and at the rate of 0.99 per-unit. The model reveals notable enhancements in stakeholder satisfaction, particularly through the integration of electric vehicles and energy trading in smart grids. It underscores the critical impact of aligning renewable energy strategies with prosumer socio-economic-environmental preferences, offering valuable insights for energy managers, policymakers, and community planners in fostering efficient, community-oriented smart grid systems. [ABSTRACT FROM AUTHOR]