# Virtual Power Plant Optimization in Smart Grids: A Narrative Review
## 3 VPP Concept and Technology
### Digital Twins' Models  
A fundamental aspect of virtual aggregation of energy assets or resources in VPPs is their modeling to understand operational constraints, energy behavior, and interaction flow [5]. Lately, the concept of the digital twin (DT) has emerged as a virtual representation of energy assets. It can successfully be used to conduct various analyses and simulation processes to study their energy behavior and energy exchanges [9]. In the energy grid, DTs are considered a disruptive technology for enhancing and maintaining smart grids and for building ML models to enhance their performance and efficiency [10].  
DTs are used to model all components of the VPP portfolio of assets (energy production, consumption, storage) and simulate behavior through complex analytics with the general objective of facilitating the integration of optimization heuristics to drive the operation closer to the defined goals [11]. The technologies associated with DTs are complex system modeling, big data prediction, ML, optimization, and agent-based techniques [12]. They involve complex data processing and models to digitize and evaluate the grid rules, understand the energy distribution flows, or the impact of decision-making [13]. For example, DT models for net zero energy buildings deal with optimization of the renewable usage considering inhabitants' comfort and constraints [14]. They are useful for empowering VPPs to manage their building portfolio towards minimizing the energy exchanges with the grid and improving the energy efficiency [15]. They are not limited to electrical energy aspects, but they can model thermal, usage, and cost aspects of various energy assets from the VPP portfolio to increase the amount of committed flexibility [16]. Simulation also can be carried out for the thermal design of a heating and cooling system of buildings and their relationship with energy usage and available energy flexibility to be used by the VPP [16].  
DTs can model the energy flexibility profiles of assets and devices such as heat pumps, EVs, PV, hot water, or gas systems that are relevant for VPPs [10]. The main challenge is to couple them with information relating to the user's wishes in terms of comfort, convenience, and well-being. A user's DT profile seamlessly incorporates their flexibility profiles, representing the selected flexibility assets, and can increase the \"smartness level\" of buildings which will lead to increased participation in energy flexibility services to be delivered by the VPP [17]. The modeling techniques' focus is not just on the average dynamics but also on modeling uncertainty, i.e., a statistical description of stochastic behavior, as it has a severe impact on any decision-making logic on VPP optimization [18]. In holistic terms, the DT can consider the consumer's profile in terms of behavior and incentive mechanisms of VPPs along with their community aggregation association and flexibility assets reflecting various commodities (electricity, biogas, heat) [19].  
Finally, DTs have been used for managing customer profiles and raising awareness about energy services [9]. Data on customer preferences, satisfaction, and behavior can help DTs to generate reports for increasing customer awareness [20]. Indeed, DTs are seen as a key asset for citizens' engagement which is very relevant for local community management and sustainability [21]. Engagement of individual households and collectively operating households in demand response and energy communities remains a challenge [111]. In addition, since smart meters themselves do not communicate with consumers, their roles in changing consumer behavior will be low. Thus, providing products and services which address concerns and provide enough benefits is pivotal for reaching the potential of VPPs and energy communities [112].