LCOE-Based Pricing for DLT-Enabled Local Energy Trading Platforms

Abstract

Support schemes like the Feed-in-Tariff (FiT) have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism to prosumerism. This democratization and decarbonization of the energy system has led to both challenges and opportunities for the system operators, paving the way for emerging concepts like local energy markets. The FiT approach has often been assumed as the lower economic bound for a prosumer’s willingness to participate in such markets but is now being phased out in several countries. In this paper, a new pricing mechanism based on the Levelized Cost of Electricity is proposed, with the intention of securing profitability for the prosumers, as well as creating a transparent and fair price for all market participants. The mechanism is designed to function on a Distributed Ledger Technology-based platform and is further set up from a holistic perspective, defining the market framework as interactions in a Cyber-Physical-Social-System. Schemes based on both fixed and variable contracts with the wholesale supplier are analyzed and compared with both the conventional FiT and to its proposed replacement options. The results show a cost reduction for the consumers and a slight loss in revenue for the prosumers compared to the FiT scheme. Comparing it to the actual suggested replacements to the FiT, however, it is clear that the pricing mechanism proposed in this study provides a substantial increase of benefits for both prosumers and consumers.

Local electricity market pricing mechanisms' impact on welfare distribution, privacy and transparency

“…The three mechanisms were compared in [25] and [26], showing the best overall performance and the highest social welfare among the market participants when using the supply-demand ratio mechanism. Many versions of the mechanism have been proposed in the literature, with extensions such as preferred participation level [31], compensation rates [30], donation systems [32], alternative pricing boundaries [33] and other incentive mechanisms [34]. There are also several examples of how this simple rule-based method can be combined with more complex market clearing algorithms, such as game-theoretic markets [35] and distributed optimisation [36].…”