On the Scalability of Decentralized Energy Management using Profile Steering

Optimizing the use of flexibility, provided by e.g. batteries and electric vehicles, provides opportunities for various stakeholders. Examples are aggregators acting on energy markets, or energy cooperations willing to maximize their self-consumption. However, with large numbers of devices that need to be scheduled, the underlying optimization problem becomes difficult. This paper investigates the scalability of a smart grid optimization approach called Profile Steering. This approach uses a hierarchical structure to perform distributed optimization. In this paper, the approach is extended with methods to accept multiple profiles at once and the possibility to prune children with little flexibility. Simulation studies with almost 20,000 households are carried out to evaluate the scalability of Profile Steering. The results show that, with the presented improvements, the required optimization time of Profile Steering scales linearly with the number of children and a speedup factor of 56 is achieved with 1000 households. Furthermore, the approach scales well across multiple computing processes.</p

Asynchronous event driven distributed energy management using profile steering

Distributed Energy Management methodologies with a scheduling approach based on predictions require means to avoid problems related to prediction errors. Various approaches deal with such prediction errors by applying a different online control mechanism, such as a double-sided auction. However, this results in two separate control mechanisms for the planning phase and the real-time control phase. In this paper, we present a two-phase approach with profile steering based control in both phases. The first phase is synchronous and uses predictions to create a planning. The second phase uses profile steering to schedule individual devices in an event driven and asynchronous manner. Simulation results show that this methodology results in an improved power quality and follows the planning better with a RMSE reduction of up to 34%. In addition, it provides more robustness to failure of connection and improves transparency of its actions to prosumers

Relating Electric Vehicle Charging to Speed Scaling with Job-Specific Speed Limits

Due to the ongoing electrification of transport in combination with limited power grid capacities, efficient ways to schedule the charging of electric vehicles (EVs) are needed for the operation of, for example, large parking lots. Common approaches such as model predictive control repeatedly solve a corresponding offline problem. In this work, we first present and analyze the Flow-based Offline Charging Scheduler (FOCS), an offline algorithm to derive an optimal EV charging schedule for a fleet of EVs that minimizes an increasing, convex and differentiable function of the corresponding aggregated power profile. To this end, we relate EV charging to processor speed scaling models with job-specific speed limits. We prove our algorithm to be optimal and derive necessary and sufficient conditions for any EV charging profile to be optimal. Furthermore, we discuss two online algorithms and their competitive ratios for a specific class objective functions. In particular, we show that if those algorithms are applied and adapted to the presented EV scheduling problem, the competitive ratios for Average Rate and Optimal Available match those of the classical speed scaling problem. Finally, we present numerical results using real-world EV charging data to put the theoretical competitive ratios into a practical perspective

GridShield—Optimizing the Use of Grid Capacity during Increased EV Adoption

With the increasing adoption rate of electric vehicles, power peaks caused by many cars simultaneously charging on the same low-voltage grid can cause local overloading and power outages. Smart charging solutions should spread this load, but there is a residual risk of incidental peaks. A decentralized and autonomous technology called GridShield is being developed to reduce the likelihood of a transformer’s fuse blowing when other congestion solutions have failed. It serves as a measure of last resort to protect the grid against local power failures from unpredicted congestion by temporarily limiting the virtual capacity of charging stations. This paper describes the technical development and demonstrates how GridShield can keep a transformer load below a critical limit using simulations and real-world tests. It optimizes grid capacity while ensuring grid reliability.</p