This paper presents an integrated methodology for decision making in smart grid investments that assesses the investment plans of stakeholders in local energy communities (LECs). Considering the energy flow exchanges of the LECs and interpreting them in terms of technical benefits and costs, this methodology indicates the most sustainable and profitable solution covering the LEC energy transition plans. A set of specialized tools capturing the energy, environmental, financial, and social impacts are integrated under a common platform called the IANOS Energy Planning and Transition (IEPT) suite. The tools evaluate a set of well-defined key performance indicators that are gathered using a cost–benefit analysis (CBA) module offering multilateral assessment. By upgrading the functionalities of specialized tools, i.e., the energy modeler INTEMA, the life cycle assessment and costing tool VERIFY, and the smart grid-oriented CBA tool, the IEPT suite evaluates the viability of different smart grid investment scenarios from a multi-dimensional perspective at the LEC level. The functionalities of the proposed suite are validated in the LEC of Nisyros island, Greece, where three smart grid-based investment scenarios of different self-consumption levels are evaluated and ranked in terms of benefits and profitability. The results highlight that for a 20-year horizon of analysis, the investment scenario where a 50% self-consumption target is achieved was more financially viable compared to the 80% and 95% scenarios, achieving values of BCR and NPV equal to EUR 2.12 and EUR 4,400,000, respectively.

Building stock retrofitting is essential to achieve the ambitious sustainability goals of the building sector due to its high energy consumption rates. The evaluation of the various building interventions shall be holistically assessed in terms of environmental and costing impact. The aim of this paper is twofold: First, it presents the innovative characteristics of a developed online tool (Virtual intEgrated platfoRm on LIfe cycle AnalYsis -VERIFY) able to perform dynamic life cycle analysis and global warming impact assessments by capitalizing on the well-known LCA and LCC methodologies, applicable in the case of building renovation. VERIFY is able to analyse dynamic life cycle inventories that consider the temporal profiles of energy consumption, and the time -dependent temperature changes, while being also interoperable in terms of exchanging data with other available energy simulation engines, or even using real-time monitoring data from sensors, processing any data time granulation. Second, the paper evaluates, from a life cycle perspective, the impact of specific energy retrofitting measures, meeting the Passive House Standard, for the case of a multi-family residential building in Athens, Greece. The proposed energy-retrofitting scenario examines actions related to the deep retrofitting of the building envelope and the upgrade of the thermal components as well as to the incorporation of clean electricity generation based on renewable energy systems; all aiming to drastically reduce the environmental impact of the building, rendering it almost near zero energy. Through the planned infrastructure installations, the primary energy needs and CO2eq emissions were reduced by 91 % and by 95 % respectively, while for a building oper-ational lifespan of 25 years, savings up to 515 k euro compared to the baseline scenario, can be achieved.

As part of the RINNO project which aims at increasing the building renovation rates in the EU, a framework solution has been developed for the selection of the best renovation scenario for a building. The RINNO solution enables users to take informed decisions through the holistic assessment of alternative scenarios and facilitates the selection of the optimum one according to their preferences. The various software tools developed comprising this framework are discussed in this work along with their integration strategy. An example is also presented where the use of the RINNO solution determines the scenario with the optimum performance.

This paper investigates numerically the deep renovation of a multi-family building in Greece to reduce dramatically its energy demand and also to incorporate renewable energy sources, rendering it a positive one; thus in position on an annual basis to offer net electricity to the grid. The examined building has 8 apartments of 75 m2 floor area each and is located in Moschato, a suburb of Athens in Greece. The goal of the present investigation is to determine the energy savings, but also to calculate the financial and environmental benefits through a life cycle analysis. The energy simulation of the building is conducted on annual basis by using a novel and detailed dynamic software tool (INTEMA.building), which is developed in the Dymola environment using the Modelica modeling language. This tool makes possible the detailed simulation of both passive and active systems in the building environment. Furthermore, it includes the control of the energy systems and can provide accurate enough results, encompassing detailed numerical models for the systems investigated, accounting for an adjustable time step of the dynamic analysis. According to the calculations, the proposed retrofitting scenario can achieve a reduction of the heating loads by 93% and of cooling loads by 78% respectively. The electrical demand for domestic hot water can be decreased by about 79%, while the electricity demand for appliances and lighting by about 60%. In terms of specific thermal needs, the specific heating demand can be reduced from 151.5 kWh/ m2 down to 10.7 kWh/m2, while the cooling specific demand from 112.6 kWh/m2 to 24.4 kWh/m2. Moreover, it is calculated that the reduction in the primary energy demand after the renovation can be up to 88%, with the building providing around 5.3 MWh of net electricity to the grid on a yearly basis through a net-metering connection. Finally, the life cycle cost analysis indicated 622 keuro savings and specific CO2 avoidance per renovated floor area in the range of 2.64 tons CO2/m2.

Building stock renovation is a major challenge towards a sustainable energy transition. In this context, there is a need for accurate and holistic assessment of retrofitting solutions. While Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methods are typically used to quantify the outcomes of a retrofit solution, these methods are highly dependent on accurate data, which is often not available in the design phase. The work presented in this paper demonstrates a building renovation assessment platform that follows a holistic approach and enables rapid but accurate consideration of several renovation scenarios. The innovation lies in the integration of two specialized tools, namely VERIFY and INTEMA.building, for lifecycle and energetic calculations, respectively. The integration offers a solution for the case in which no operational data are available. After the detailed presentation of the platform, the architecture and the offered functionality, a building renovation problem is considered as a demo case. A typical low-efficiency Greek building is examined while interventions are assumed, such as insulation of external wall, replacement of glazing surfaces, as well as heat pump and photovoltaic installation. Results showcase a significant reduction in lifetime CO2 emissions and primary energy of around 785 tons and 700 MWh, respectively. At the same time, the economic viability is ensured with estimated savings of 225 k€ during project lifecycle.

The harmonization between the variable rate of energy production in the era of massive renewable energy penetration is a major challenge in an open, competitive and resilient electricity market. As a result, there is an increasing need for modern pricing schemes, which will effectively incentivize willing users to modify their energy consumption pattern to meet this objective. Current energy pricing schemes (e.g. real time pricing) treat all users the same, and do not adequately compensate for behavioral changes, thus mitigating the behavioral change dynamics. In this paper, we propose a Community Real Time Pricing (CRTP) scheme together with an Energy Community Formation Algorithm (ECFA), where users are clustered in Virtual Energy Communities (VECs) according to: (i) their level of flexibility in modifying their Energy Consumption Curve (ECC), and (ii) their relationships in Online Social Networks (OSNs), modelling peer-pressure capabilities. We show that CRTP with ECFA can simultaneously achieve considerable reduction in the system's energy cost and greater aggregated users' welfare than with the state-of-the-art real time pricing. CRTP ECFA adopts a truly fair pricing policy, as each user is rewarded exactly according to his/her individual contribution in reducing system costs, thus promoting the desired behavioral change.

Liberalized electricity markets, smart grids and high penetration of renewable energy sources (RESs) led to the development of novel markets, whose objective is the harmonization between production and demand, usually noted as real time of flexibility markets. This necessitates the development of novel pricing schemes able to allow energy service providers (ESPs) to maximize their aggregated profits from the traditional markets (trading between wholesale/day-ahead and retail markets) and the innovative flexibility markets. In the same time, ESPs have to offer their end users (consumers) competitive (low cost) energy services. In this context, novel pricing schemes must act, among others, as automated demand side management (DSM) techniques that are able to trigger the desired behavioral changes according to the flexibility market prices in energy consumption curves (ECCs) of the consumers. Energy pricing schemes proposed so far, e.g. real-time pricing, interact in an efficient way with wholesale market. But they do not provide strong enough financial incentives to consumers to modify their energy consumption habits towards energy cost curtailment. Thus, they do not interact efficiently with flexibility markets. Therefore, we develop a flexibility real-time pricing (FRTP) scheme, which offers a dynamically adjustable level of financial incentives to participating users by fairly rewarding the ones that make desirable behavioral changes in their ECCs. Performance evaluation results demonstrate that the proposed FRTP is able to offer a 15%–30% more attractive trade-off between the stacked profits of ESPs, i.e. the sum of the profits from retail and flexibility markets, and the satisfaction of the consumers.

Progressive electric utilities are gradually digitizing their business in order to be able to efficiently manage their customer portfolio and cope with the increasing competition in the retail market. Thus, advanced S/W tools and platforms are needed, like the Research Algorithms and Business Intelligence Tool (RABIT) proposed in this paper. RABIT provides advanced data analytics services (i.e. advanced search, profilers, recommenders) targeted to the utility's administrative users (e.g. business analysts). In addition, it disposes: i) dynamic and behavioural pricing models linked with various innovative energy programs, and ii) algorithms for the creation and dynamic adaptation of virtual energy communities. RABIT can also automatically analyze exhaustive business/strategy 'what-if' scenarios by running parameterized system-level simulations. Performance evaluation results show that a utility company can exploit RABIT in order to: i) reduce costs for purchasing energy from wholesale market, ii) enhance its end users' welfare, iii) increase its business profits, and iv) increase its portfolio's energy efficiency.

A major challenge in an open and competitive electricity market that exploits renewable energy sources is the alignment between the variable rate energy production and the ad hoc energy consumption of the end users. In this context, there is a need for modern pricing schemes that will be able to effectively incentivize willing users towards modifying their energy consumption pattern based on current conditions. Existing pricing schemes treat all users the same, thus mitigating the behavioral change dynamics. To address this deficiency, we propose a Community Real Time Pricing (C-RTP) model, where users form communities and are charged according to the energy behavior of their entire community. The proposed C-RTP system is compared against the most widely accepted model in the literature, the RTP pricing model, and is shown to achieve lower energy cost (10%-15%) without sacrificing the end users' welfare or the profits of the energy service provider.

In the smart grid era, various actors and S/W agents need to exchange vast amounts of data towards meeting the communication requirements of the emerging smart energy network infrastructures and the efficient operation of the smart grid system. In this paper, we consider a hierarchical smart grid architecture, in which a novel aggregator market entity is introduced, which acts as an intermediary between the various market/grid operators and the small-scale energy prosumers. A trade-off analysis is undertaken to study the problem of minimizing the utilized network bandwidth without compromising the efficient smart grid system performance.

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%.

The phasing out of Feed-in-Tariff has made domestic prosumers reluctant to invest in Renewable Energy Sources in most part of Europe. The limited availability of excess electrical power from domestic prosumers, 4 -10 kW, does not give them any bargaining power when dealing with energy suppliers or utilities. This paper describes a tool that is being developed under the umbrella of an EU-funded project - VIMSEN to help small prosumers participate into a decentralized energy market and to help meet the requirement of EU2020 legislation. This tool can be used by energy aggregators to represent groups of individual prosumers that can form the basis of their customer base. It uses a dynamic decision support system that can change the composition of the cluster of prosumers to satisfy an electricity demand requirement over a specific period of time.

Today’s electricity system is undergoing a transformation from a model of centralized electricity generation, to a more decentralized paradigm, where a large number of small energy prosumers (i.e. both producers and consumers) generate energy and may participate in the energy market. In these markets, energy is usually traded at a time prior to the time of delivery in an exchange, based on forecasts of the production and the consumption, and the cost for the prosumers is related to the accuracy of these forecasts, through the application of penalties when an imbalance appears. In this paper we study the problem of orchestrating the energy prosumers into virtual clusters, in order to participate in the market as a single entity and to reduce the total energy cost, through the reduction of the total relative forecasting inaccuracies. Using a real dataset of 33 prosumers located in Greece, we study different clustering algorithms, including spectral, genetic and an adaptive algorithm. The performance evaluation results show that significant cost reduction may be achieved, through the association of the prosumers into groups.

The current centralized framework of energy production and distribution prevents small electricity producers from participating actively in the electricity market. Their participation to larger energy associations so as to strengthen their position necessitates new Information & Communication Technologies (ICT), architectures and business models. The VIMSEN ("Virtual Microgrids for Smart Energy Networks") project proposes a highly dynamic and distributed framework for future energy markets, modifying the existing energy market ecosystem and introducing new market players. The framework is primarily based on the concept of Virtual Micro-Grids (VMGs) and on the active participation of renewable energy prosumers to the energy market while a number of ICT systems provide advanced functionalities at VMG aggregators' and prosumers' sides. This paper aims to present the high-level VIMSEN system architecture focusing on the identification of system entities, their functionalities and the required communication interfaces.

The Smart Energy Grid concept aims to exploit Information and Communication Technologies (ICT) towards making the energy sector more secure, reliable and efficient, while the electricity markets are rapidly becoming more liberalized with new business actors/models being introduced. In particular, passive energy consumers are being transformed into active energy prosumers (i.e. both producers and consumers), while energy aggregation/services companies are emerging as intermediaries in the so called "Internet of Energy" arena. Prosumers need to have their energy assets efficiently managed and participate in the market independently of their size and negotiating power, while aggregators aim at maximizing prosumers' benefits by representing them as a single big power entity in the wholesale energy market. This paper introduces the Virtual MicroGrid (VMG) concept, in which multiple energy prosumers are orchestrated into bigger associations towards optimizing the association's benefits. An innovative decision support system platform is presented showcasing that the management of aggregated energy resources can outperform state-of-the-art solutions that manage resources at the individual prosumer's level. The platform's implementation is based on virtualization techniques and a wide range of functionalities are described, tested and validated. Datasets from 37 real-life prosumers are used and results of various decision-making algorithms show that under different system operation contexts, dynamic formation of prosumers' groups (clusterings) can provide remarkable energy savings and monetary profits to the end users.

In this paper we discuss optical network unit (ONU) based traffic prediction in Ethernet passive optical networks (EPONs). The technique utilizes least-mean-square polynomial regression for the estimation of incoming traffic and adaptive least-mean-squares filtering for the estimation of the EPON cycle duration. Given these estimates, the ONU successfully predicts its bandwidth requirements at the next available transmission opportunity and communicates this prediction, rather than its actual buffer occupancy, to the optical line terminal (OLT). The proposed scheme is assessed via simulations and it is demonstrated that a delay improvement of 30 % can be achieved without modifying the dynamic bandwidth assignment process at the OLT. In addition, we further explore aspects of traffic prediction combined with a max-min fair bandwidth redistribution scheme at the OLT. Initial results show that the combination of the ONU-based prediction and the OLT-based fair bandwidth redistribution further improves the delay.

The authors propose a novel traffic prediction method for the minimisation of packet delay in Ethernet passive optical networks. The method relies on traffic monitoring at the optical network units (ONUs) and utilises readily available traffic information to predict the accumulated burst size of each respective ONU in the following cycle. They demonstrate that a significant delay enhancement can be accomplished by reporting the predicted, rather than the current, burst size to the optical line terminal (OLT). The author's simulation results show that a delay improvement of over 25% can be expected by the proposed method without modifying the well-established interleaved polling scheme with adaptive cycle time dynamic bandwidth assignment scheme at the OLT.

We propose a traffic prediction algorithm that reduces the packets delay in Ethernet Passive Optical Networks (EPONs). The algorithm relies on Multi-Point Control Protocol (MPCP) message and traffic monitoring at the Optical Network Units (ONUs) and utilizes the monitoring information to predict the accumulated burst size using higher order least-mean-square polynomial approximations. The simulation of the algorithm shows that it achieves a delay improvement of over 30% without any further modification in the communication and bandwidth assignment procedure of the EPON.