Dynamic loss aversion and hyperbolic time-weighted decision-making in short-term self-scheduling of local multi-carrier energy systems with environmental sustainability programs

The emergence of local multi-carrier energy systems (LMCESs) and their participation in upstream energy markets as virtual power plants represent significant advancements toward a more sustainable energy society. Traditional economic models used in the LMCES self-scheduling problem often assume purely rational decision-making. To address this, the present study proposes an advanced behavioral economic-environmental model based on a modified Prospect Theory framework. The model integrates hyperbolic time discounting to capture temporal preferences in valuing future monetary outcomes and incorporates dynamic loss aversion to better reflect the operator's changing sensitivity to different loss levels. The behavior of the LMCES operator in response to price volatility is modeled using two distinct price scenarios, each assigned a specific probability, enabling the framework to capture how operators subjectively distort probabilities when making decisions under risk. Furthermore, the model incorporates environmental sustainability considerations by enabling participation in emission reduction programs through incentive-based schemes. The effectiveness of the proposed approach is demonstrated by comparison with both traditional Prospect Theory and Expected Theory. Results show that integrating behavioral biases and environmental preferences into the self-scheduling process leads to more robust and sustainable operational decisions.