Scaling: managing a large number of distributed battery energy storage systems

Abgottspon, Hubert; Schumann, René; Epiney, Lucien; Werlen, Karl

doi:10.1186/s42162-018-0023-5

Cited by 6 publications

(5 citation statements)

References 20 publications

(21 reference statements)

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“…Equal SoC In the context of this strategy, we look at some observations from Abgottspon et al (2018). They analyze how to control a Battery Energy Storage System (BESS) and observe many parameters.…”

Section: Strategiesmentioning

confidence: 99%

Simulation of a Cellular Energy System including hierarchies and neighborhoods

2022

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The massive use of small energy resources and storage units causes a transition from a traditionally centralized to a decentralized energy system. To structure and coordinate the emerging changes in the energy system, the concept of Energy Cells (ECs) was developed. Several ECs can be combined to form a hierarchically superordinate EC. These hierarchically superordinate ECs can in turn be combined and thus form a complex hierarchy. An EC encapsulates coherent parts of an energy system and can communicate as well as exchange energy with other ECs at a different or the same level. It follows the idea of local balance of energy provision and demand. A network of ECs forms a Cellular Energy System (CES). In this paper, we develop a concept for modeling and simulating a CES. We accomplish this by beginning with atomic components like consumers, producers, and storage units and aggregating them with Hierarchical Controllers (HCs). Such a hierarchically structured energy system is part of various proposals. However, we are able to add neighborhood relations by introducing Local Controllers (LCs). This is more realistic and also opens many degrees of freedom for control strategies in such a system. Following the recursive structure of the CES itself, we define recursive functions for visiting the CES architecture and realizing various control strategies. We evaluate our approach in a series of partially randomized scenarios, showing notable differences in the performance of the CES regarding different control strategies in a larger example. We also provide a theoretical analysis of the computational complexity of the suggested approach.

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Section: Strategiesmentioning

confidence: 99%

Simulation of a Cellular Energy System including hierarchies and neighborhoods

2022

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“…ICT, energy, economic and social) (Schloegl et al 2015) Many works have focused on the co-simulation of smart grids by integrating simulators of power grids with ICT communication aspects, so-called Cyber-Physical Energy System (CPES) (Georg et al 2013;Garau et al 2018;Pan et al 2016;. Co-simulation has been widely applied also to integrate several models in order to represent and describe the planning of new RES deployment (Reinbold et al 2019;Steinbrink et al 2019;Schiera et al 2019) or to study the effects of novel control strategies to exploit energy flexibility for demand response applications (Song et al 2017;Bhattarai et al 2016;Abgottspon et al 2018;Mazzarino et al 2021). To ease the coupling of simulators, researchers have started defining standards for co-simulation, such as Functional Mock-up Interface (FMI) (Blochwitz et al 2011), and co-simulation frameworks, such as Mosaik (Schütte et al 2011) and HELICS (Palmintier et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem

et al. 2022

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The transition to a low-carbon society will completely change the structure of energy systems from a standalone hierarchical centralised vision to cooperative and distributed Multi-Energy Systems. The analysis of these complex systems requires the collaboration of researchers from different disciplines in the energy, ICT, social, economic, and political sectors. Combining such disparate disciplines into a single tool for modeling and analyzing such a complex environment as a Multi-Energy System requires tremendous effort. Researchers have overcome this effort by using co-simulation techniques that give the possibility of integrating existing domain-specific simulators in a single environment. Co-simulation frameworks, such as Mosaik and HELICS, have been developed to ease such integration. In this context, an additional challenge is the different temporal and spatial scales that are involved in the real world and that must be addressed during co-simulation. In particular, the huge number of heterogeneous actors populating the system makes it difficult to represent the system as a whole. In this paper, we propose a comparison of the scalability performance of two major co-simulation frameworks (i.e. HELICS and Mosaik) and a particular implementation of a well-known multi-agent systems library (i.e. AIOMAS). After describing a generic co-simulation framework infrastructure and its related challenges in managing a distributed co-simulation environment, the three selected frameworks are introduced and compared with each other to highlight their principal structure. Then, the scalability problem of co-simulation frameworks is introduced presenting four benchmark configurations to test their ability to scale in terms of a number of running instances. To carry out this comparison, a simplified multi-model energy scenario was used as a common testing environment. This work helps to understand which of the three frameworks and four configurations to select depending on the scenario to analyse. Experimental results show that a Multi-processing configuration of HELICS reaches the best performance in terms of KPIs defined to assess the scalability among the co-simulation frameworks.

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“…Distributed Battery Energy Storage Systems (BESSs), thanks to their fast response and high ramp rate can furnish energy flexibility to the TSO or the DSO. The benefits that several residential BESSs can offer when providing ancillary services are demonstrated by some researches [4][5][6]. In [4] it is proven that BESSs can decrease the local demand and the power peaks in low-voltage grids.…”

Section: Introductionmentioning

confidence: 99%

“…In [5] it is verified that an aggregator of residential BESSs can gain revenue by providing ancillary services. In [6] an energy utility manages many distributed BESSs by using a heuristic algorithm based on an aggregation and disaggregation method that allows delivering market services. In [7] the possibility of using residential Photovoltaic (PV)-battery systems for the provision of up and down-regulation has been verified by using a nonlinear method based on a Mixed-Integer Linear Programming (MILP) optimization model.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Privacy Preserving Distributed Parallel Optimization for a Large-Scale Aggregation of Prosumers With Residential PV-Battery Systems

Dolatabadi

Siano

2020

IEEE Access

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A novel scalable and privacy-preserving distributed parallel optimization that allows the participation of large-scale aggregation of prosumers with residential PV-battery systems in the market for the ancillary service (ASM) is proposed in this paper. To consider both reserve capacity and reserve energy, day-ahead and real-time stages in the ASM are considered. A method, based on hybrid Variable Neighborhood Search (VNS) and distributed parallel optimization is designed for the day ahead and realtime optimization. Different distributed optimization methods are compared and designed and a new distributed optimization method based on Linear Programming (LP) is designed that overcomes previous methods based on integer and Quadratic programming (QP). The proposed LP-based optimization can be easily coded up and implemented on microcontrollers and connected to a designed Internet of Things (IoT) based architecture. As confirmed by simulation results, carried out considering different realistic case studies, both day-ahead and real-time proposed optimization methods, by allocating the computational effort among local resources, are highly scalable and fulfil the privacy of prosumers.

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Scaling: managing a large number of distributed battery energy storage systems

Cited by 6 publications

References 20 publications

Simulation of a Cellular Energy System including hierarchies and neighborhoods

Simulation of a Cellular Energy System including hierarchies and neighborhoods

A comparison study of co-simulation frameworks for multi-energy systems: the scalability problem

A Scalable Privacy Preserving Distributed Parallel Optimization for a Large-Scale Aggregation of Prosumers With Residential PV-Battery Systems

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