Quantifying Device Flexibility With Shapley Values in Demand Side Management

The value of flexibility in demand side management (DSM) is a measure that is hard to quantify. However, having such a value is important in various areas, from determining research directions to cost allocation and writing policy. In this work, we propose a method to determine the value of flexible assets using average marginal contributions, based on the Shapley value. We apply this method in a smart home peak shaving DSM context. Our results show that the Shapley value of an electric vehicle (EV) is the largest in this context, 27% larger than the Shapley value of the runner-up, a battery energy storage system. This highlights that when doing research or writing policy for DSM in the residential sector, EVs are the most important area to focus on. Additionally, we show that if the right device configurations are chosen, there can be strong synergy between devices, i.e., the flexibility valuation of combinations of devices can be higher than the sum of their individual contributions. The results of this work provide intuition for which device configurations and combinations can achieve such synergy. The presented framework can not only be used to valuate flexible assets in DSM in the context of a smart home, but can also be applied to larger scale energy systems

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

A survey of offline algorithms for energy minimization under deadline constraints

Modern computers allow software to adjust power management settings like speed and sleep modes to decrease the power consumption, possibly at the price of a decreased performance. The impact of these techniques mainly depends on the schedule of the tasks. In this article, a survey on underlying theoretical results on power management, as well as offline scheduling algorithms that aim at minimizing the energy consumption under real-time constraints, is given

Optimal DPM and DVFS for frame-based real-time systems

Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVFS) are popular techniques for reducing energy consumption. Algorithms for optimal DVFS exist, but optimal DPM and the optimal combination of DVFS and DPM are not yet solved. In this article we use well-established models of DPM and DVFS for frame-based systems. We show that it is not sufficient—as some authors argue—to consider only individual invocations of a task. We define a schedule that also takes interactions between invocations into account and prove—in a theoretical fashion—that this schedule is optimal

Robust peak-shaving for a neighborhood with electric vehicles

Demand Side Management (DSM) is a popular approach for grid-aware peak-shaving. The most commonly used DSM methods either have no look ahead feature and risk deploying flexibility too early, or they plan ahead using predictions, which are in general not very reliable. To counter this, a DSM approach is presented that does not rely on detailed power predictions, but only uses a few easy to predict characteristics. By using these characteristics alone, near optimal results can be achieved for electric vehicle (EV) charging, and a bound on the maximal relative deviation is given. This result is extended to an algorithm that controls a group of EVs such that a transformer peak is avoided, while simultaneously keeping the individual house profiles as flat as possible to avoid cable overloading and for improved power quality. This approach is evaluated using different data sets to compare the results with the state-of-the-art research. The evaluation shows that the presented approach is capable of peak-shaving at the transformer level, while keeping the voltages well within legal bounds, keeping the cable load low and obtaining low losses. Further advantages of the methodology are a low communication overhead, low computational requirements and ease of implementation

On the interplay between global DVFS and scheduling tasks with precedence constraints

Many multicore processors are capable of decreasing the voltage and clock frequency to save energy at the cost of an increased delay. While a large part of the theory oriented literature focuses on local dynamic voltage and frequency scaling (local DVFS), where every core’s voltage and clock frequency can be set separately, this article presents an in-depth theoretical study of the more commonly available global DVFS that makes such changes for the entire chip. This article shows how to choose the optimal clock frequencies that minimize the energy for global DVFS, and it discusses the relationship between scheduling and optimal global DVFS. Formulas are given to find this optimum under time constraints, including proofs thereof. The problem of simultaneously choosing clock frequencies and a schedule that together minimize the energy consumption is discussed, and based on this a scheduling criterion is derived that implicitly assigns frequencies and minimizes energy consumption. Furthermore, this article studies the effectivity of a large class of scheduling algorithms with regard to the derived criterion, and a bound on the maximal relative deviation is given. Simulations show that with our techniques an energy reduction of 30% can be achieved with respect to state-of-the-art research

Optimization of Multi-Energy Systems Using the Profile Steering Coordination Framework

Scalable planning and control of individual devices within multi-energy systems is important to support the energy transition. However, multi-energy systems are complex due to relations between different energy carriers on different levels. This paper extends the Profile Steering algorithm with support for such multi-energy systems using distributed optimization, in which individual components can be added. As concrete application, a method to perform load shedding and curtailment, to balance a local district heating network, is presented. Our evaluation shows that a multi-energy system, consisting of a buffer, a CHP, and a heat pump can be optimized within reasonable time and leads to a reduction in export of 51.4%

On a Reduction for a Class of Resource Allocation Problems

In the resource allocation problem (RAP), the goal is to divide a given amount of a resource over a set of activities while minimizing the cost of this allocation and possibly satisfying constraints on allocations to subsets of the activities. Most solution approaches for the RAP and its extensions allow each activity to have its own cost function. However, in many applications, often the structure of the objective function is the same for each activity, and the difference between the cost functions lies in different parameter choices, such as, for example, the multiplicative factors. In this article, we introduce a new class of objective functions that captures a significant number of the objectives occurring in studied applications. These objectives are characterized by a shared structure of the cost function depending on two input parameters.We show that, given the two input parameters, there exists a solution to the RAP that is optimal for any choice of the shared structure. As a consequence, this problem reduces to the quadratic RAP, making available the vast amount of solution approaches and algorithms for the latter problem.We show the impact of our reduction result on several applications, and in particular, we improve the bestknown worst-case complexity bound of two problems in vessel routing and processor scheduling fromO(n2) to O(nlogn). Summary of Contribution: The resource allocation problem (RAP) with submodular constraints and its special cases are classic problems in operations research. Because these problems are studied in many different scientific disciplines, many conceptual insights, structural properties, and solution approaches have been reinvented and rediscovered many times. The goal of this article is to reduce the amount of future reinventions and rediscoveries by bringing together these different perspectives on RAPs in a way that is accessible to researchers with different backgrounds. The article serves as an exposition on RAPs and on their wide applicability in many areas, including telecommunications, energy, and logistics. In particular, we provide tools and examples that can be used to formulate and solve problems in these areas as RAPs. To accomplish this, wemake three concrete contributions. First, we provide a survey on algorithms and complexity results for RAPs and discuss several recent advances in these areas. Second, we show that many objectives for RAPs can be reduced to a (simpler) quadratic objective function, which makes available the extensive collection of fast and efficient algorithms for quadratic RAPs to solve these problems. Third, we discuss the impact that RAPs and the aforementioned reduction result canmake in several application areas