# Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
## 3 Emerging Technologies Enabling Positive Energy Districts
### Business Model Challenges in Positive Energy Districts  
Effective business models for creating PEDs are crucial. A lack of coherent business models covering the needs of PEDs has been identified [72]. A complete financial strategy is often also missing. Regional and national grants together with private investment are mainly used for implementing PEDs. Innovative business and financial models constitute a significant challenge that needs to be addressed [70]. Factors that require further consideration in the PED business model are the following:  
1. _Economic Feasibility_: This is challenging because of high initial costs and the requirements of future financial robustness and sustainability. Although PEDs have a wide range of possible revenue streams, many cannot yet be fully materialized due to missing regulatory frameworks [73]. The cost of PEDs needs to further decrease, including technology introduction and integration.
2. _Balancing costs and benefits_: An effective business model should balance costs and benefits successfully to attract investment and safeguard profitability [2]. PED business models need to consider dynamic pricing, peak shaving (reducing energy consumption at times of high energy demand), new markets to be opened, and new products to be created.
3. _Complex stakeholder interactions_: Including multiple stakeholders with diverse interests and needs is challenging and requires adequate communication and collaboration strategies for aligning the various stakeholder goals and guaranteeing effective implementation. Cultural differences and social acceptance are likely to impact PED employment. Scaling up or replication of successful PEDs is challenging because different urban areas have different cultural, spatial, and social contexts. PEDs have a number of shared characteristics, despite the fact that they are very specific to their local context. [PERSON] et al. [74], for example, argue that \"_real-life PEDs tend to go beyond the frames set by the definitions because the concept fails to consider the contextual factors that are inherent in them_\". Another factor that is important for replication is difference in Key Performance Indicators (KPIs). As stakeholders may use various KPIs for PED assessment, agreement regarding value and impact may increase the challenges [75]. However, it is important to extract the maximum replication potential of a PED in the early design phase in order to allow tailor-made solutions for other local contexts [42].
4. _Local community and end-user engagement_: PED business models should contain strategies for local community engagement. Their concerns need to be taken into consideration in order to promote acceptance and participation. End-user and public awareness regarding the advantages of PEDs needs to be catered for [76].
5. _Supportive policies and regulations_: These are imperative for the scalability and dispersion of PEDs. The regulatory landscape reveals a noteworthy challenge. Business models usually need to adapt to existing national and regional regulations despite the fact that local policies may not support innovative energy solutions [76]. Governance structures need to be flexible to allow for effective management of the complexity and interdependencies of PEDs. PED business models should be adaptable and in clude strategies for replication and scalability in different urban contexts, which are distinctive due to varying geographic, historical, and socio-economic conditions [5].
* _Integrating contemporary technologies_: Integrating renewable energy systems (e.g., solar PV, wind turbines, and geothermal energy), energy storage solutions, smart grids, hydrogen fuel cells, and biofuels has been proven to be a complex task.
* _Business models_: When creating business models, technological advancements and technology adoption should be taken into consideration to ensure the seamless integration of energy management optimization. The technological challenges include energy simulation, modeling, and performance assessment [2,77].