Hierarchical multi-(voltage-)level grid control strategies are an appropriate
design concept for the coordination of future TSO/DSO- and
DSO/DSO-interactions. Hierarchical approaches are based on the aggregation of
decentralized ancillary service potentials, represented by converter-coupled,
communicable active and reactive power flexibility providing units (FPU) (e.g.
wind turbines) at vertical system interfaces. The resulting PQ-polygon made
available by the DSO for a potential request of ancillary service flexibilities
by the TSO is called feasible operation region (FOR). A monetarization of the
FOR is necessary for the implementation as operational degree of freedom within
higher-level grid control. This paper presents an approach for the
monetarization of the FOR in the context of a hierarchical multi-level
flexibility market by a cost structure using metadata from population based
optimization methods. Multiple FPU flexibility polygons at a single bus are
aggregated for a reduction of the search space dimensions. The main
contribution of the proposed method is the cost-optimal disaggregation of a
flexibility demand to the single FPUs within the aggregated FPU by a mixed
integer linear program (MILP). Therefore, a local flexibility market
considering bids for the active and reactive power flexibilities by the FPUs
stakeholders is assumed. The approach is applied within a case-study of the
Cigr\'e medium voltage system.

Monetarization of the Feasible Operation Region based on a Cost-Optimal Flexibility Disaggregation.

Hierarchical grid control strategies are an appropriate design concept for the coordination of future transmission system operator (TSO) and distribution system operator (DSO) interactions. Hierarchical approaches are based on the aggregation of decentralized ancillary service potentials, represented by converter-coupled, communicable active and reactive power flexibility providing units (FPU, e.g. wind turbines) at vertical TSO/DSO system interfaces. The resulting PQ-polygon made available by the DSO for a potential request of ancillary service flexibilities by the TSO is called feasible operation region (FOR). A monetarization of the FOR is necessary for the implementation as operational degree of freedom within TSO grid control. In the context of a local DSO and global TSO market, this article presents an approach for the monetarization of the FOR by a cost structure using metadata from population based aggregation methods. At the local DSO market free bids for the active and reactive power flexibilities by the FPUs stakeholders are assumed. Within the aggregation method multiple FPU flexibility polygons at a single bus are aggregated for a reduction of the search space dimensions. Thereby, the main contribution of the proposed method is the cost-optimal disaggregation of a flexibility demand to the single FPUs within the aggregated FPU by a mixed integer linear program. The approach can be simply adapted for the coordination of DSO/DSO-interactions regarding hierarchical multi-level grid control strategies. 

Monetarization of the Feasible Operation Region of Active Distribution Grids Based on a Cost-Optimal Flexibility Disaggregation

Attaining highly secure and safe operation of the grid with acceptable voltage levels has become a difficult issue for electricity companies that must adopt remedial actions. The usage of a PV solar farm inverter as a static synchronous compensator (or PVSTATCOM device) throughout the night has recently been proposed as a way to enhance the system performance. In this article, the novel artificial rabbits’ optimization algorithm (AROA) is developed for minimizing both the daily energy losses and the daily voltage profile considering different 24 h loadings. The novel AROA is inspired from the natural surviving strategies of rabbits. The novel AROA is tested on a typical IEEE 33-node distribution network including three scenarios. Different scenarios are implemented considering PV/STATCOM allocations throughout the day. The effectiveness of the proposed AROA is demonstrated in comparison to differential evolution (DE) algorithm and golden search optimization (GSO). The PVSTATCOM is adequately allocated based on the proposed AROA, where the energy losses are greatly reduced with 54.36% and the voltage deviations are greatly improved with 43.29%. Moreover, the proposed AROA provides no violations in all constraints while DE fails to achieve these limits. Therefore, the proposed AROA shows greater dependability than DE and GSO. Moreover, the voltage profiles at all distribution nodes all over the daytime hours are more than the minimum limit of 95%.

/pdf/an-artificial-rabbits-optimization-to-allocate-pvstatcom-for-305a9tgu.pdf

An Artificial Rabbits’ Optimization to Allocate PVSTATCOM for Ancillary Service Provision in Distribution Systems

Power grids are transitioning from an infrastructure model based on reactive electronics towards a smart grid that features complex software stacks with intelligent, pro-active and decentralized control. As the power grid infrastructure becomes a platform for software, so does the need for a reliable roll-out of software updates on a large scale. In order to validate resilient large-scale software roll-out protocols, corresponding test beds are needed, which mirror not only ICT networks, but also include the actual software being deployed, and show the interaction between the power grid and the ICT network during the roll-out, and especially during roll-out failures. In this paper, we describe the design implementation of a large-scale co-simulation test bed that combines ICT and power grid simulators. We pay specific attention to the details of integrating containerized software in the simulation loop.

/pdf/large-scale-co-simulation-of-power-grid-and-communication-3z5o5k0r3o.pdf

Large-Scale Co-Simulation of Power Grid and Communication Network Models with Software in the Loop

Future smart grids can and will be subject of systematic attacks that can result in monetary costs and reduced system stability. These attacks are not necessarily malicious, but can be economically motivated as well. Emerging flexibility markets are of interest here, because they can incite attacks if market design is flawed. The dimension and danger potential of such strategies is still unknown. Automatic analysis tools are required to systematically search for unknown strategies and their respective countermeasures. We propose deep reinforcement learning to learn attack strategies autonomously to identify underlying systemic vulnerabilities this way. As a proof-of-concept, we apply our approach to a reactive power market setting in a distribution grid. In the case study, the attacker learned to exploit the reactive power market by using controllable loads. That was done by systematically inducing constraint violations into the system and then providing countermeasures on the flexibility market to generate profit, thus finding a hitherto unknown attack strategy. As a weak-point, we identified the optimal power flow that was used for market clearing. Our general approach is applicable to detect unknown attack vectors, to analyze a specific power system regarding vulnerabilities, and to systematically evaluate potential countermeasures.

/pdf/towards-reinforcement-learning-for-vulnerability-analysis-in-3jzrjulfg8.pdf

Towards reinforcement learning for vulnerability analysis in power-economic systems

Liberalization of the energy system sets the way towards market-based solutions for ancillary service provision. Local reactive power markets are envisioned to achieve more economically and technically efficient reactive power provision to solve voltage control problems in future distribution and transmission grids. However, market-based reactive power procurement is a difficult and yet unsolved problem. This survey provides a comprehensive overview of the characteristics and hardships of reactive power markets. That is followed by a literature overview of reactive power market design, including local markets and markets on system operator level. Further, methods how to analyse reactive power markets are discussed, focusing on market power, game theoretical approaches, Reinforcement Learning, and manipulation of reactive power markets. From this overview, trends and current research gaps are derived and some general research recommendations are given to serve as a guideline for future research in the field of reactive power markets.

/pdf/reactive-power-markets-a-review-15t7oq86.pdf

Reactive Power Markets: a Review

Modern cyber-physical systems (CPS), such as our energy infrastructure, are becoming increasingly complex: An ever-higher share of Artificial Intelligence (AI)-based technologies use the Information and Communication Technology (ICT) facet of energy systems for operation optimization, cost efficiency, and to reach CO2 goals worldwide. At the same time, markets with increased flexibility and ever shorter trade horizons enable the multi-stakeholder situation that is emerging in this setting. These systems still form critical infrastructures that need to perform with highest reliability. However, today's CPS are becoming too complex to be analyzed in the traditional monolithic approach, where each domain, e.g., power grid and ICT as well as the energy market, are considered as separate entities while ignoring dependencies and side-effects. To achieve an overall analysis, we introduce the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool that is able to analyze the dependencies between domains in CPS by attacking them. It eschews pre-configured domain knowledge, instead exploring the CPS domains for emergent risk situations and exploitable loopholes in codices, with a focus on rational market actors that exploit the system while still following the market rules.

Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence

In the last years, diverse agent-based concepts for voltage control in distribution grids were presented in literature. All these approaches are developed manually. Up to now, no tailoring approach has been presented to adapt these concepts to different – likewise specific – grid situations. As the effectiveness of voltage control schemes is highly dependent on the specific grid situation, this can result in multiple problems with regard to performance, applicability and expandability of the proposed agent-based control. The ideal case would be a customizable agent system that is automatically tailored to the grid it shall be applied to, considering its specific characteristics. To address that complex task, this paper proposes an approach for the modular composition of voltage control agents by recombining existing concepts from literature to new fully functional agent systems. This approach makes the first move to an optimized and scenario-specific creation of agent-based control systems that are specifically designed and automatically tailored to a given power system.

/pdf/towards-modular-composition-of-agent-based-voltage-control-4gnt1ysvd6.pdf

Towards modular composition of agent-based voltage control concepts

Abstract The shift from conventional power plants on transmission level to distributed energy resources in the distribution grids requires procedures to enable efficient and economic reactive power exchange across different voltage levels. In this paper, we propose a multi-level reactive power market that enables reactive power provision from distributed energy resources to higher voltage levels. Each grid operator operates a local reactive power market and offers local reactive power potential, as an intermediary, to superordinate grid operators by passing on its aggregated cost curve and flexibility range. As a result, there is a low requirement of communication between the market participants and each grid operator is free to choose its local market rules as well as the optimization algorithm used. First results from a case study show that our decentralized market approach can realize an economically efficient multi-level reactive power provision that is close to a centrally computed optimal solution, without violating local grid constraints. 

/pdf/design-and-evaluation-of-a-multi-level-reactive-power-market-195ovvxn.pdf

Design and evaluation of a multi-level reactive power market

Asset-management accounts for a significant share of grid operators’ influenceable costs, which are under high economic pressure due to deregulation. In asset-management, visual inspections are the main measure to determine asset conditions. In the context of digitalization, we propose a concept to acquire additional information about the condition of distribution substations and to derive flexible inspection intervals from this information, in contrast to fixed intervals that are common in scientific literature and practice. Thus, knowledge about the substations increases, inspection intervals can be expanded dynamically, and significant cost savings are possible, which we demonstrate by the example of a German distribution grid operator.

https://www.mdpi.com/1996-1073/13/15/3875/pdf

Thomas Wolgast

Papers

Reactive Power Markets: a Review

Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence

Towards modular composition of agent-based voltage control concepts

Design and evaluation of a multi-level reactive power market

Dynamic Inspection Interval Determination for Efficient Distribution Grid Asset-Management