Energy & Environment

The RESONANCE project is creating a software framework for plug-and-play development of solutions for demand-side flexibility management (DSFM) of distributed and small-scale assets. The Framework contains three catalogues of software (micro) services, including Resource Manager (RM) Catalogue, Customer Energy Manager (CEM) Catalogue and Aggregation and Market Integration Catalogue. Additionally, an important part of the Framework are Data and Service Marketplaces that facilitate the co-creation within a vibrant cross-sector energy ecosystem and this way promote the sustainable uptake of DSFM solutions at a large scale. STEcon is involved in the economic and business modelling and analysis, towards defining and assessing user engagement and incentive mechanisms for Demand Response and sustainable business models. The group is also involved in project activities related to energy performance and optimization as well as to the validation of the overall RESONANCE approach.

Energy communities are crucial for the renewable energy transition as they empower local stakeholders to actively participate in clean energy generation and distribution, fostering decentralization and resilience in energy infrastructure. Additionally, these communities drive innovation, promote energy efficiency, and facilitate knowledge sharing, accelerating the adoption of renewable energy sources and paving the way for a sustainable energy future. Financing energy communities through traditional schemes and instruments may be challenging due to the specific characteristics of each community and the feasibility of renewable energy solutions. The Innovative Supporting Schemes for Community-Led Energy Transition (ISLET) project aims to support collaboration between public authorities, private investors, and citizens to develop renewable energy communities with an integrated approach, facilitating the emergence and growth of renewable community energy projects on Mediterranean small islands. AUEB contributes to this project with an innovative methodology for assessing financing mechanisms and instruments, considering the specificities of each community, including societal aspects. Finally, the project aims to develop business and financing plans tailored to three pilot islands in the Mediterranean Sea: Astypalea (Greece), Cres (Croatia), and Procida (Italy).

Automated Demand Response (ADR) can facilitate residential customers to effectively reduce their energy demand and make savings in a simple way, provided that appropriate incentives are offered to them. Most often, incentives involved in ADR contracts are statically defined and assume full customer rationality, thus hindering sustained customer enrollment to them of customers with other characteristics (e.g. altruism). In the context of CHARGED project, we derived appropriate (and personalized) incentives for ADR contracts, so that non-fully rational customers are compensated even when information for consumer utilities is not available. In case such information is hidden, we assumed that customers provide feedback on their satisfaction from direct endowments, albeit sustaining energy-consumption reduction. Moreover, we considered the case where customers may strategically lie on their satisfaction from ADR incentives, so as to self-optimize. We mathematically model the customer and the utility company’s problems and solve them algebraically or in a distributed manner. Furthermore, based on customer feedback on appropriate endowments for different energy-consumption reductions, we proposed an algorithm that can find the optimal set of satisfied targeted customers, which achieved the total desired energy-consumption reduction at the minimum endowment cost. Based on numerical evaluation and simulation experiments, we showcase the validity of our analytical framework in realistic scenarios and that, for the case of hidden information, customer feedback is adequate for calculating incentives that can lead to successful DR campaigns.

The efficient evolution of the electricity grid depends on the active engagement of the end users (citizens) by means of their participation in demand response programs. More specifically, the retailer may announce dynamic prices during the peak periods, targeting to incentivize its clients to decrease their consumption or shift their elastic loads to the off-peak periods. In this context, this project (group internal research project) focuses on the profiling of the electricity consumers, i.e., the formulation of their demand with respect to the price and the environmental temperature. The project firstly classified the end-users according to their demand shifting behaviour and then developed two relevant models: the former is designed to capture the demand shifts within different periods in a day, in response to the values of the dynamic prices and temperatures in all those periods, while the latter assumes that the consumption during each period depends only on the price and the temperature of this particular period.

In constructing customer profiles based on historical data, the above models are fitted to a publicly available energy consumption dataset derived form a dynamic-pricing program at the city of London, using least square linear regression. Our findings suggest that the clustering of the users according to their shifting history and the subsequent assignment of the suitable model to each one of them, improves the forecast accuracy and achieves a percentage error lower than 4% (referring to the aggregate demand of the whole retailer’s clientele). Additionally, an algorithm is designed which may be utilized by the retailer for meeting its obligation for a balanced portfolio. The algorithm takes as input the models’ forecasts and computes the values of the dynamic prices to be applied such that the collective reaction of the end-users leads to the desired level of aggregate consumption. The models may be extended to capture further parameters that affect the consumption behaviour of the users such as their income, the members of the households and their social position (working or retired). Additionally, they may be modified for providing forecasts with higher granularity than the two-slotted separation of the planning horizon (peak and off-peak periods) in in their current form, and distinguish between the working days and the weekends targeting to improve their forecast accuracy. 

In the context of EC projects Wattalyst and OPTi our team developed methodologies for designing incentive-based DR programs as well as for ADR contracts. On the latter, two different approaches were developed and evaluated in a prototype tool targeted to utility companies. The prototype has been developed with the collaboration of IBM India. In the Fixed versus Learning-based Incentives in ADR Contracts approach, each consumer is modelled based on the modification of the optimal consumption schedule in the case of participation in DR, which leads to estimation of the incentives necessary for the consumer to actually participate. For each consumer there is a minimum incentive value that triggers him to participate. On the contrary, in Net Benefit-based Incentives in ADR Contracts approach each consumer is assumed to act rationally by choosing the consumption schedule that maximises her total Net Benefit. The approaches are robust with regard to sources of supply (i.e. are agnostic in that sense) in order to take into account energy supply from Distributed Resources as well.

The Price Based Flexibility Profiling (PBFP) tool is part of the Nobel Grid suite of tools for Demand-side Management, and more specifically manual demand-response schemes. It produces a profile for each customer, reflecting real-time demand flexibility as a function of multiple parameters, such as environmental context/ conditions, energy retail prices at peak and off-peak periods and individual/group preferences. These profiles can be used for calculating the flexibility obtained by each endpoint for any price and outdoor temperature during the peak and off-peak periods, as well as, for calculating incentive-compatible rewards to participants based on economic theory. 

AUEB has lead the work of Business Modelling analysis for emerging Smart Grids in the context of project NobelGrid. A novel methodology has been developed that is based on a use case and product driven value network analysis and business modelling canvas generation. The methodology will be applied to the projects' pilots and will help us realise and evaluate the market potential of the Smart Grid technologies and the resulting interactions among the market players, namely DSOs, Aggregators, Retailers and Prosumers. Furthermore, in the next phase of this work (in progress) AUEB develops Cost Benefit Analysis tools for both Smart Grid networks (in project NobelGrid) and District Heating and Cooling networks (in project OPTi). Once evaluated the tools will be publicly released for the energy community.