The complex nature of cyber-physical energy systems (CPES) makes systematic testing of new technologies for these setups challenging. Co-simulation has been identified as an efficient and flexible test approach that allows consideration of interdisciplinary dynamic interactions. However, basic coupling of simulation models alone fails to account for many of the challenges of simulation-based multi-domain testing such as expert collaboration in test planning. This paper illustrates an extended CPES test environment based on the co-simulation framework mosaik. The environment contains capabilities for simulation planning, uncertainty quantification and the development of multi-agent systems. An application case involving virtual power plant control is used to demonstrate the platform’s features. Future extensibility of the highly modular test environment is outlined.
Unlocking and managing flexibility is an important contribution to the integration of renewable energy and an efficient and resilient operation of the power system. In this paper, we discuss how the potential of a fleet of battery-electric transportation vehicles can be used to provide frequency containment reserve. To this end, we first examine the use case in detail and then present the system designed to meet this challenge. We give an overview of the tasks and individual sub-components, consisting of (a) an artificial neural network to predict the availability of Automated Guided Vehicles (AGVs) day-ahead, (b) a heuristic approach to compute marketable flexibility, (c) a simulation to check the plausibility of flexibility schedules, (d) a multi-agent system to continuously monitor and control the AGVs and (e) the integration of fleet flexibility into a virtual power plant. We also present our approach to the economic analysis of this provision of a system-critical service in a logistical context characterised by high uncertainty and variability.
With the transition towards renewable energy resources, the impact of small distributed generators (DGs) increases, leading to the need to actively stabilize distribution grids. DGs may be organized in virtual power plants (VPPs), where DGs’ schedules must be coordinated to enable the VPP to act as a single plant. One approach to solving this problem is using multi-agent systems (MAS) to offer autonomous, robust, and flexible control methods. The coordination of such systems requires communication between agents. The time required for this depends on communication characteristics, determined by the underlying communication infrastructure. In this paper, we investigate communication influences for the wireless technologies CDMA450 and LTE Advanced on the fully distributed optimization heuristic COHDA, which is used to perform optimized scheduling for a VPP. The use case under consideration is the adaptation of schedules to provide flexibility for regional congestion management for delivery on a regionalized ancillary service market (rAS). We investigate the scalability of the VPP and the effects of disturbances in the communication infrastructure. The results show that the optimization duration of COHDA can be influenced by the underlying communication infrastructure and that this optimization method is applicable to a limited extent for product delivery of rASs.
The smart grid is a highly relevant application area for distributed algorithms. Many of these algorithms use a predefined topology to control the information exchange between the distributed entities. Consequently, this exchange topology has a strong impact on the performance of the distributed algorithm. For several algorithms their distributed nature is part of the algorithm itself. Furthermore, the properties of the virtual links defined by the exchange topology are determined by the characteristics of the underlying communication infrastructure (CIS). This work aims to design optimized exchange topologies that support certain distributed algorithms that can be used for reactive scheduling in smart distribution grids. For this purpose, supportable algorithms have to be identified and their communication requirements have to be derived. In addition, relevant properties and a suitable degree of abstraction of the CIS have to be determined. Finally it has to be investigated whether properties of the smart grid and the distributed energy resources (DER) and controllable loads are significant factors that can be used to achieve an increased performance of the supported algorithm.
The multi-purpose usage of battery energy storage systems (BESSs) increases the exploitation of their flexibility potential. This can be further enhanced when a large number of small BESSs are combined into a swarm and marketed collectively by an aggregator. To this end, a unified representation of remaining flexibility for each BESS is needed that meets the requirements of both, a multi-purpose usage and a distributed swarm design. In this work, we present a compact model which we call abstract multi-purpose-limited flexibility (Amplify). It can be used by an aggregator to determine how much flexibility remains after accepting obligations and includes an integrated detection of conflicts in the planned schedule of a BESS. It is shown that the model is quickly computable and does not need much data volume during transmission.
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