The Vision of Self-Management in Cognitive Organic Power Distribution Systems
Abstract
:1. Introduction
- Conventional regulatory schemes incentivise grid reinforcement instead of using information and communication technology (ICT);
- Full transparency in low and medium voltage grids is non-existent at present. Over the next years, near to full transparency is planned on the medium voltage level, on the low voltage level, this will be even farther in the future;
- The smart meter roll-out is delayed due to information technology (IT) security requests and a limitation to many customers in the first roll-out phases;
- A resilient and continuously available communication infrastructure to connect all sensors and actors has yet to be built;
- Present “smart energy” products request significant effort to configure, parameterise, connect devices, etc.;
- It is not yet entirely clear if from a full system perspective this shift is beneficial regarding costs, reliability, resiliency, etc.
2. State of the Art
2.1. Home Energy Management Systems and Distribution Management Systems
2.2. Methodical Foundations from the Fields of Autonomic and Organic Computing
2.2.1. Self-Configuration
2.2.2. Self-Organisation
2.2.3. Self-Optimisation
2.2.4. Self-Healing
3. A Concept for Organic Distribution Management Systems
3.1. Key Research Question
3.2. System Model
3.2.1. Simulation Framework
3.2.2. Observable and Controllable Parameters
3.3. Evaluation
4. Conclusions and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AC | Autonomic Computing |
CM | Control Mechanism |
DG | Distributed Generators |
DMS | Distribution Management System |
EPS | Electric Power System |
HEMS | Home Energy Management Systems |
ICT | Information and Communication Technology |
IT | Information Technology |
MARL | Multi-agent Reinforcement Learning |
MAS | Multi-agent System |
MG | Microgrid |
ML | Machine Learning |
ODiS | Organic Distribution System |
O-DMS | Oorganic Distribution Management System |
O-HEMS | Organic Home Energy Management System |
OC | Organic Computing |
PV | Photovoltaic |
RL | Reinforcement Learning |
SASO | Self-adaptive and Self-organising |
XCS | Extended Classifier System |
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Use Case | Description |
---|---|
C1 | Manual (Dis)connection of a new electrical asset: Self-configuration is required after detection of new assets, as well as self-configuration of the grid model, operational algorithms, local parameters, communication configuration. The new power grid topology needs to be validated based on self-organisation. |
C2 | Continuous anomaly detection, automatic background process: The system needs to continuously monitor the data streams for anomalies and inconsistencies as could be caused by mal-parameterisation, human failure, or cyber-attacks. If anomalies are detected, the underlying data or control schemes need to be adjusted accordingly. |
C3 | (Dis)connection of a new agent: Every O-HEMS or O-DMS (agent) must perform self-configuration to adapt to the new overall system. This further requires self-organisation for determining the best possible system structure. |
O1 | Self- and environment-awareness (including state estimation and forecasting): The basis for optimisation of electrical grids is the knowledge of the relevant grid variables. A state estimation ensures that such knowledge is available, even when a low number of real-time measurements is available, which is typically the case for distribution grids. Forecasting provides projections into the future, which can be strongly beneficial for control strategies. In principle, both, O-HEMS and O-DMS, must be self- and environment-aware. |
O2 | Normal operation and self-optimisation: During normal operation, key performance indicators are inside their defined boundaries. Nevertheless, self-optimisation of agents can improve their conditions further, e.g., to minimise system losses or increase self-consumption of energy. |
O3 | Voltage limit violation (no circuit breaker tripping): High load, high DG, or a sub-optimal switching configuration can cause voltage limit violations in a grid zone. Self-optimising agents need to choose between different measures to push the voltage back inside limits. Measures can be self-optimisation via load management, active and reactive power control of DG, flexibilities/storage, switching state reconfiguration, or transformer tap adjustments. |
O4 | Thermal line/transformer limit violation (no circuit breaker tripping): The current flowing through grid assets is bound by thermal limits, which should not be violated for longer than a specific period or failures can occur. Similar to the use case O2, agents need to be aware of potential limit violations and use available measures to alleviate the limit violation. |
C/O5 | Unreliable communication for the asset(s): Communication between the agents can become unreliable, e.g., faulty measurement devices produce unreliable measurements that should not be used further. The communication network can become unstable, e.g., exhibit packet loss. Agents need to identify unreliable data streams and self-adapt accordingly. |
C/O6 | Interrupted communication for the asset(s): If technical issues appear in the communication network used for self-coordination, the exchange of information becomes unreliable or interrupted completely. Similarly to C/O5, agents need to self-adapt, e.g., by using an alternative channel of communication or by changing their control scheme from central to local control strategies. |
H1 | Self-protection: In the case of an asset failure or a manual mal-configuration, events such as blackouts can occur. For such cases, a self-protecting O-DMS prepares contingency strategies so that a minimum number of customers are affected. |
H2 | Self-healing/reconfiguration: If the self-protection is not successfully performed, a self-healing and reconfiguration scheme must be run. The scheme can trigger individual parts of a grid to operate as autonomous islands without connection to the higher voltage level (self-islanding) and may require changes in control strategies of O-HEMS etc. (e.g., switching to local control strategies). The process includes decoupling from the interconnected grid, a black start, islanding operation with suitable control strategies and re-synchronisation when self-islanding is no longer optimal. |
H3 | Self-healing under impaired ICT: Impaired ICT connections pose an additional challenge for self-healing schemes. O-DMS and O-HEMS need to evaluate the limited control options and prioritise operational limit violations, e.g., allow for thermal overloading up to a certain time frame to avoid a (partial) blackout. |
H4 | Blackout in the communication grid: A blackout in the communication grid prevents coordination among the different agents. The O-DMS needs to evaluate and activate alternative means of communication. If that is not possible, the agents need to switch to local control strategies automatically. |
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Loeser, I.; Braun, M.; Gruhl, C.; Menke, J.-H.; Sick, B.; Tomforde, S. The Vision of Self-Management in Cognitive Organic Power Distribution Systems. Energies 2022, 15, 881. https://doi.org/10.3390/en15030881
Loeser I, Braun M, Gruhl C, Menke J-H, Sick B, Tomforde S. The Vision of Self-Management in Cognitive Organic Power Distribution Systems. Energies. 2022; 15(3):881. https://doi.org/10.3390/en15030881
Chicago/Turabian StyleLoeser, Inga, Martin Braun, Christian Gruhl, Jan-Hendrik Menke, Bernhard Sick, and Sven Tomforde. 2022. "The Vision of Self-Management in Cognitive Organic Power Distribution Systems" Energies 15, no. 3: 881. https://doi.org/10.3390/en15030881