Prodromos Makris

A highly-dynamic and distributed operational framework for smart energy networks

The intense environmental concern regarding the CO2 footprint of the energy sector entails the higher Renewable Energy Sources (RES) share in the energy mix of the global production profile. Currently, energy is produced and distributed under a centralized framework preventing small electricity producers from participating in the electricity market, unless they are part of larger energy associations, the administration of which necessitates the exploitation of new energy and ICT technologies. The VIMSEN project introduces a highly dynamic and distributed market framework, in which new market players will emerge and the current energy market ecosystem will be modified. The framework is primarily based on the concept of Virtual Micro-Grids (VMGs) and on the active participation of renewable energy prosumers. This position paper aims to analyze the VIMSEN ecosystem in a conceptual way and introduce novel market actors as well as their responsibilities that enable the implementation of beyond state-of-the-art ICT frameworks and business models in future liberalized energy markets.

Micro grid scheduling policies, forecasting errors, and cooperation based on production correlation

We present scheduling policies that can be used by Micro Grids (MGs) with the possibility of energy production, consumption and storage, in the context of a novel Smart Grid architecture. Assuming a Day-Ahead market, we investigate the management of resources so as to achieve higher profit for the MGs under various operating scenarios. The possibility of cooperation among MGs in the presence of forecast errors is also studied, demonstrating that it can lead to more intelligent and profitable operation of the MG resources. The profits of cooperation among MGs whose production patterns have positive, zero, or negative correlation are assessed.

Demonstration of the smart energy neighbourhood management system in the VIMSEN project

The VIMSEN project, part of the European R&D 7th Framework Programme, started in February 2014. The VIMSEN project aims to develop, implement and demonstrate a new energy management operation and business model using the concept of virtual micrigrids, able to increase the market participation of prosumers at neighbourhood level. The new control system (VIrtual Microgrids for Smart Energy Networks - VIMSEN) will be developed to manage and control distributed energy resources, stationary storage devices, electric vehicles charging infrastructure, building appliances, etc. The VIMSEN concept enables the large-scale integration of distributed energy resources by utilizing aggregated demand response to the maximum. Two demonstration sites are committed with VIMSEN: Sedini, on the island of Sardinia, Italy, and Athens, Greece. The results and conclusions coming from the demonstration activities will provide the basis for the elaboration of recommendations for future virtual microgrids for smart energy networks. This paper presents the VIMSEN architecture and the use cases.

Efficient Clustering of DERs in a Virtual Association for Profit Optimization

The Feed In Tariff policy (FIT) used for accelerating renewable energy investments cannot be retained as a sustainable business model for the future smart energy grid. It is also evident that the current centralized electricity market prevents small or very small energy producers, who usually generate energy by renewable means, to participate. In this paper, addressing the aforementioned problems, at first, we present a decentralized architecture (Virtual DER Clusters), where small Distributed Energy Sources (DERs) are united in coalitions, each participating in the market as a single entity. Then, efficient clustering algorithms are proposed based on a min-max optimization policy in order to dynamically derive the cluster that best satisfies coalitions goals. Maximization in the sense of increasing as much as possible the profits of small-scale energy producers. Minimization in the sense of creating dynamically most competitive clusters. In this paper, three clustering policy schemes are discussed, each presenting different advantages with respect to the contradictory benefits between DERs and power utilities. From the examined policies, a fair sharing allocation scheme seems to be a good compensator between the electricity market and the small-scale players.

Efficient Gateways Placement for Internet of Things with QoS Constraints

In the Internet of Things (IoT) era, a large number of data packets are exchanged among devices over the Internet. IoT (smart) end devices forward their traffic to the Internet, either by direct communication to LTE networks, or by multi-hop transmissions to a specific gateway. Acquiring both types of communication capabilities would be unnecessarily costly for IoT end devices. Instead, to lower the overall cost, only devices performing as gateways could be fully equipped with such capabilities, while the rest of the devices could have simple low cost wireless transmitters for forwarding the traffic towards a gateway. Towards this end, the decision on gateway placement is critical to provide low cost and Quality of Service (QoS). To address this design problem, we present an Integer Linear Programming (ILP) that minimizes the total cost of the network with respect to the deployed devices, while achieving mandatory QoS requirements. Using this formulation we obtain solutions for random topologies that exhibit the effectiveness of our approach in terms of network cost and efficiency.

VIMSEN — Smart tool for energy aggregators

The subsidy feed in tariff policy, which has been adopted in the past few years, for accelerating renewable energy investments, cannot be retained as a sustainable business model for the future smart energy grid. This is mainly due to the fact that this policy increases the energy cost, especially when the amount of the energy generated by renewable sources is not negligible compared to the traditional ones, as is expected for the near future. Additionally, the current centralized electricity market prevents small or very small energy producers, who usually generate energy by renewable means - photovoltaic units or wind turbines - to participate. There is a role for energy aggregators to act on behalf of these small prosumers so that they could sell their excess energy collectively in order to get a better price. To help the tasks of the aggregator a trading tool is required that will facilitate the clustering of prosumers who are able to offer energy at a certain time during the day based on contractual agreements. The VIMSEN project is set-up to produce such a system that will allow the aggregator to select prosumers and form virtual micro-grids by virtue of smart communication systems. VIMSEN is part of the European R&D 7th Framework Programme with the aims to develop, implement and demonstrate a new energy management and business model system using the concept of virtual microgrids, to enable an increase in the market participation of prosumers at neighbourhood and community levels.

Renewable energy prosumers clustering towards target aggregated prosumption profiles based on recursive predictions

This paper introduces a decision making framework for aggregating Microgrids and/or other small energy producers and consumers (i.e. prosumers) into groups, whose purpose is to participate in liberalized electricity markets as single entities. The aggregator is able to offer aggregated Renewable Energy Source (RES) units to the wholesale market, in ways that are more efficient than individual prosumers acting alone. We first present the proposed framework and information flow among the involved market entities. We then focus on the problem of finding the set of prosumers whose aggregate prosumption profile can best fit a given target pattern requested by a market actor. We propose a linear autoregressive forecasting algorithm and a genetic clustering algorithm, which can easily adapt to the requirements set by the various use cases. Numerical results show that the aggregator can produce clusters in real time improving the average deviation from the target pattern by up to 50%.