The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within an energy community (EC). Battery aging is accounted for in the optimization model through the state of health (SOH). The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS.

A number of microeconomic choice models are currently applied to demonstrate systematic biases in energy consumer behavior. The models highlight the hidden potential of energy savings from policies that target the so-called behavioral anomalies. Nevertheless, whether these patterns are repeatable or not is not clear, because the efforts to determine the transferability or generalizability of these models are practically nonexistent. This paper uses a unique collection of empirical data from five EU countries collected within the CONSEED project to refine and develop further the standard consumer decision model, validate it for policy purposes, and elaborate on its transferability between countries. The pooled samples allow for a more reliable investigation of the relative importance of the factors influencing consumers’ attitudes and beliefs towards energy investment decisions. Based on the statistical tests conducted to evaluate the “transferability” of the pooled models (i.e., the possibility of creating a “universal” model of EE from the pooled model), it can be argued that the models are transferable in specific cases since attitudinal factors and demographic characteristics play a significant role. Although the pooled models are validated, any extrapolation of the above-mentioned findings to specific populations in terms of “space” (i.e., country) and “target” (e.g., sectors and technologies) should be approached with caution from a policy perspective.

The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within energy community (EC). The model accounts for battery cyclic degradation and reduces the state of health (SOH) over the project lifetime. The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS.