Modelling and characterization of flexible resources
Utilization of flexible resources can be a cost-effective alternative to grid investment [1], and also enhances the security of electricity supply: energy availability, power capacity, reliability of supply and power quality [2]. To enable large-scale utilization of flexible resources, it is useful to establish unified definitions of the properties which characterize them. This is important both for modelling the resources, but also for facilitating the information exchange between stakeholders. Degefa et al. [3] conceptualized a comprehensive classification framework for characterizing flexible resources.
Modelling the flexible resources in the distribution grid is a useful way of demonstrating their potential impact. The most central flexible resources considered in CINELDI so far, has been demand side flexibility in building operation [4]–[6] and households [7]–[9], battery storage systems [10]–[18] and electric vehicle (EV) charging [19]–[23].
Battery storage systems can be utilized as flexible resources in the power system. Although many such systems are presently considered to be too expensive solutions in many cases, the prices are expected to drop. Thus, battery storage systems have been considered for many case studies in CINELDI: optimization of battery system operation, as an alternative to grid reinforcement, for enhancing security of supply, cost-optimal operation in prosumer villas, considering battery degradation and optimal operation of such systems in large buildings, such as a football stadium, for example.
Fast charging stations for EVs is another important resource and can be considered as both a flexibility resource and as a load causing grid problems. As a resource, EV charging can be used both for reactive power supply and as a demand-side spatial flexibility resource.
The BATTPOWER toolbox was also developed in collaboration with CINELDI [24]. It is a multi-period AC optimal power flow (OPF) solver which takes various flexible resources such as stationary energy storage systems and EV charging into account and aims to be highly computationally efficient compared with traditional OPF solvers [25].
References:
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