• Energy Research

The increased penetration of Distributed Energy Resources (DERs) in electricity markets has given rise to a new category of energy players, called Aggregators, whose role is to ensure fair remuneration for energy supplied by DERs, and support the smooth feeding of the intermittent energy produced into the distribution network. This paper presents a software solution, described as a design pattern, that governs the interaction between an Aggregator and DERs, leveraging blockchain technology to achieve a higher degree of decentralization, data integrity and security, through a properly designed, blockchain-based, smart contract. Thus, the proposed solution reduces the reliance on intermediaries acting as authorities, while affording transparency, efficiency and trust to the energy exchange process. Thanks to the underlying blockchain properties, generated events are easily observable and cannot be forged or altered. However, blockchain technology has inherent drawbacks, i.e., mainly the cost of storage and execution, hence our solution provides additional strategies for limiting blockchain usage, without undermining its strengths. Moreover, the design of our smart contract takes care of orchestrating the players, and copes with their potential mutual disagreements, which could arise from different measures of energy, providing an automatic decision process to resolve such disputes. The overall approach results in lower fees for running smart contacts supporting energy players and in a greater degree of fairness assurance.

We face a decentralized renewable energy production scenario, where a large number of small energy producers, i.e., prosumers, contribute to a common distributor entity, who resells energy directly to end-users. A major challenge for the distributor is to ensure power stability, constantly balancing produced vs consumed energy flows. In this context, being able to provide quick restore actions in response to unpredictable unbalancing events is a must, as fluctuations are the norm for renewable energy sources. To this aim, the high scalability and diversity of sources are crucial requirements for the said balancing to be actually manageable. In this study, we explored the challenges and benefits of adopting a blockchain-based software architecture as a scalable, trustless interaction platform between prosumers’ smart energy meters and the distributor. Our developed prototype accomplishes the energy load balancing service via smart contracts deployed in a real blockchain network with an increasing number of simulated prosumers. We show that the blockchain-based application managed to react in a timely manner to energy unbalances for up to a few hundred prosumers.

Advanced smart grids have several power sources that contribute with their own irregular dynamic to the power production, while load nodes have another dynamic. Several factors have to be considered when using the owned power sources for satisfying the demand, i.e., production rate, battery charge and status, variable cost of externally bought energy, and so on. The objective of this paper is to develop appropriate neural network architectures that automatically and continuously govern power production and dispatch, in order to maximize the overall benefit over a long time. Such a control will improve the fundamental work of a smart grid. For this, status data of several components have to be gathered, and then an estimate of future power production and demand is needed. Hence, the neural network-driven forecasts are apt in this paper for renewable nonprogrammable energy sources. Then, the produced energy as well as the stored one can be supplied to consumers inside a smart grid, by means of digital technology. Among the sought benefits, reduced costs and increasing reliability and transparency are paramount.

This paper discusses an approach to evaluating the separation of concerns for an object-oriented software system. For assessing this separation, the developer is asked to specify the nature of classes through annotations. Automatic identification of some structural characteristics (e.g., inheritance, libraries, synchronisation) is used to appraise the composition and intertwining of concerns inside a class.