Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

Abstract

The massive integration of distributed energy resources in power distribution systems in combination with the active network management that is implemented thanks to innovative information and communication technologies has created the smart distribution systems of the new era. This new environment introduces challenges for the optimal operation of the smart distribution network. Local energy markets at power distribution level are highly investigated in recent years. The aim of local energy markets is to optimize the objectives of market participants, e.g., to minimize the network operation cost for the distribution network operator, to maximize the profit of the private distributed energy resources, and to minimize the electricity cost for the consumers. Several models and methods have been suggested for the design and optimal operation of local energy markets. This paper introduces an overview of the state-of-the-art computational intelligence methods applied to the optimal operation of local energy markets, classifying and analyzing current and future research directions in this area.

From Smart to Sustainable to Grid-Friendly: A Generic Planning Framework for Enabling the Transition Between Smart Home Archetypes

“…Fairer approaches have been proposed, though these require establishing some local energy market structure [26], [27]. Aside from the infrastructural upgrades and the inadequate energy literacy of end-users, there is no clear consensus on how to best implement such a market [28], [29]. Currently, one very rarely encounters flexibilitybased approaches that are reasonably simple to implement and that can achieve positive outcomes without giving too much authority to the DSO or too much responsibility to end-users.…”

“…4. At each iteration, the planning is split into two subproblems: current day, which is subject to objective F 1 according to (27), and the remainder of the year (expressed by the flexible set D rem ), which is subject to the modified objective F mod 1 , representing the expansion of the daily objective to a yearly basis, according to (29). The former represents the certainty region and the latter the uncertainty region.…”

Optimal Management of the Energy Flows of Interconnected Residential Users

“…Finally, Ghiani et al [27] classified EMSs into (i) classical and exact, (ii) heuristic and metaheuristic and (iii) artificial intelligent solution. An indepth analysis was provided by Georgilakis [28]. The exploitation of EMSs has been the object of several research activities.…”

Digital Transformation of Energy Companies: A Colombian Case Study

“…Works in [42][43][44][45][46] show how the use of modern digital technologies such as Big Data, blockchain, Artificial Intelligence (AI), Internet of Things (IoT), within energy industries facilitates the development of smarter energy grids and concepts that may offer more efficient and innovative approaches to energy use. AI, IoT, machine learning, blockchain, among others, are digital technologies that have been involved at different levels and stages of energy industry: resource modelling [47], production capacity prediction [48], economic load dispatch [49], demand-side response [50], maintenance management [51], integration of distributed energy resources [52,53], smart grids [54], among others. One can find more detailed applications such as in [55], who developed a new decentralized Peer-to-Peer (P2P) energy trading platform to overcome challenges such as keeping a fair balance between economic efficiency and information privacy, inter-temporal dependencies created with the incremental use of storage devices, and implementation of blockchain for P2P trading that can facilitate transactions in secured and fraud-resilient scenarios.…”