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

Rethinking Safety and Security of the Energy System for a Green Future

Nations across the world are transitioning their energy systems from fossil fuels to renewable sources at a rapidly accelerating pace. This has huge consequences for how we balance supply and demand in our electricity grid. The shift to renewables also entails a shift from highly centralised production, in large-scale fossil fuel power plants, to vastly distributed renewable sources at scales ranging from photovoltaic home systems to huge offshore wind farms. We argue that this transition requires us to radically rethink the safety and security of the energy grids and the whole supply chain that our modern society depends on. We will move from a well-understood heavily centralised infrastructure to one that is much harder to control, because of physical remoteness (e.g., offshore windfarms), the vast amount of decentralised assets, and because of a much more volatile and dynamic energy production level. This vastly increases our dependence on digital Operational Technology (OT) to control and monitor production for supply and demand balancing, and to, e.g., sense disruptions that require maintenance crews to be sent to remote locations. Couple that with a required change in behaviour of large consumers (industry) and small prosumers (households) to deal with a much more dynamic energy market and it is clear that we face big challenges in safety and security for the energy transition. In this position paper, we argue the need for a research agenda for rethinking how we manage the safety and security of our energy infrastructure

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

Distributed management of energy communities using stochastic profile steering

This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer's profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer's local energy management system. The outcome of the proposed method optimizes the average value of the community's objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability.</p