Abstract
This paper presents a novel solution integrated into the Eneida DeepGrid® platform for real-time voltage and active power estimation in low voltage grids. The tool utilizes smart grid infrastructure data, including historical data, real-time measurements from a subset of meters, and exogenous information such as weather forecasts and dynamic price signals. Unlike traditional methods, the solution does not require electrical or topological characterization and is not affected by observability issues. The performance of the tool was evaluated through a case study using 10 real networks located in Portugal, with results showing high estimation accuracy, even under scenarios of low smart meter coverage. © The Institution of Engineering and Technology 2023.

Abstract
Artificial intelligence (AI) brings great potential but also risks to the electricity industry. Following the EU's current regulatory proposal, the EU Regulation for Artificial Intelligence (AI Act), there will be direct, potentially adverse effects on companies of the electricity industry in Europe and beyond, as well as those active in the development of AI systems. In this paper, we develop a replicable framework for estimating compliance costs for different electricity market agents that will need to comply with the numerous requirements the AI Act imposes. The electricity systems of Austria, Greece and Switzerland are used as case-studies. We estimate annual, aggregated costs for electricity market agents ranging from less than one million to almost 200 million Euros per country, depending on compliance costs scenarios. Results suggest that a profit growth of 10% through AI utilization is needed to offset the highest added compliance cost of the AI Act on electricity market agents. Eventually, we further show how to assess the regional differences of these costs added to system operation, providing spatially disaggregated compliance costs estimates that consider the structural differences of the electricity industry within 26 Swiss cantons.

Abstract
Traditional retail business models price electricity using volumetric tariffs, which charge customers for the unit of energy consumed. These tariffs were designed for passive consumers with low flexibility. In this paper, we argue that these volumetric tariffs are unsuitable for prosumers with high flexibility since they are unable to adequately value the flexibility of their distributed energy resources in multiple electricity markets. This reduces the interest of prosumers participating in aggregators' business models. To address this issue, we propose a new business model for aggregators of prosumers, based on the concept of energy-as-a-service. In this business model, prosumers pay a monthly fee for aggregators to represent and optimize them in multiple wholesale electricity markets, including in energy and ancillary service markets. The monthly fee is computed by a new technoeconomic simulation framework proposed in this paper, which can also be used to estimate the profitability of the new business model from the perspectives of both the aggregator and prosumers. Our experiments on a portfolio of real prosumers from Australia show that the new business model maximizes the economic benefits of both the aggregator and prosumers by increasing the average profit of the aggregator by 438% and reducing the average electricity cost of prosumers from $583/year to $0 when compared to two of the most common retail business models available in the Australian market. In other words, the economic benefit for prosumers is free electricity. In addition to this benefit, the new business model also provides simplicity and predictability to prosumers, as they are offered a guaranteed outcome before providing the services.

Abstract
Aggregators are acknowledged as key agents to enable the active participation of household and commercial distributed energy resources (DER) in electricity markets. In recent years, many researchers and practitioners have been working on the development of diverse network-secure and network-insecure bidding strategies to support the participation of DER aggregators in electricity markets. An example of this is the extensive work developed by the authors of this paper in various R&D projects with industry. This paper builds on the experience gained in previous works and its main contribution is a thorough comparison between these strategies, resulting in an extensive discussion of their pros and cons. The discussion compares the economic and network security performance of the strategies, as well as their communication, computational, and data privacy requirements. To discuss and quantify these aspects, we formulate, implement, and test various bidding strategies on a real-world MV-LV distribution network with 2 aggregators and 522 customers for multiple DER scenarios. The discussion of the results provides realistic and valuable information on the pros and cons of each strategy, helping energy system stakeholders to understand which strategy may better fit their needs.

Abstract
Achieving carbon neutral power systems is pushing for higher penetration of distributed energy resources (DER) in existing distribution systems. Accordingly, sophisticated, yet, practical tools for the optimal operation and management of active distribution systems (ADS) are in high need. In response to this necessity, this paper presents a novel and scalable tool for ancillary services procurement by distribution system operators (DSOs). The developed tool takes into consideration the inter-temporal and variable nature of DER in an uncertainty-aware approach. This tool is also suited for real-world implementation with large ADS, as it adopts a sequential linearization approach. As such, it allows DSOs to procure flexibility optimally from DERs embedded in ADS in the day-ahead operation planning timeframe, where congestion and voltage issues are managed.

Abstract
The increasing number of Power-Electronic (PE) interfaced devices in the new generation of distribution systems results in concerns about the power quality of modern grids. Besides the loads, the harmonic-injecting devices are increasingly penetrating the generation, storage, and delivering levels of energy dispatch systems in the microgrids and the LV networks which can be easily reflected in the primary distribution systems. As an economic, applicable, and efficient solution, the passive filters can be optimized and added to the grid to absorb the harmonics. Furthermore, in the presence of controllable devices such as PE-interfaced DGs and storage units, a coordination strategy can be implemented to actively decrease the effect of the nonlinear loads. Accordingly, the idea of a virtually-hybrid filter can be developed by the use of passive filters and the coordinated active harmonic filtering strategy. In this paper, by providing an explanation for the developed coordination strategy of active filters, the probabilistic techno-economic planning of virtually-hybrid filters is studied considering the different combinations of the linear and nonlinear loads in a modern primary distribution system. Simulation results have proved that the proposed method is capable of minimizing harmonic distortions and grid loss by the use of the optimal passive filters and the suggested coordination strategy of the active devices. © 2023 IEEE.