Abstract
Renewable Energy Communities consist in an emerging decentralized market mechanism which allows local energy exchanges between end-users, bypassing the traditional wholesale/retail market structure. In that configuration, local consumers and prosumers gather in communities and can either cooperate or compete towards a common objective, such as the minimization of the electricity costs and/or the minimization of greenhouse gas emissions for instance. This paper proposes data analytics modules which aim at helping the community members to schedule the usage of their resources (generation and consumption) in order to minimize their electricity bill. A day-ahead local wind power forecasting algorithm, which relies on state-of-the-art Machine Learning techniques currently used in worldwide forecasting contests, is in that way proposed. We develop furthermore an original method to improve the performance of neural network forecasting models in presence of abnormal wind power data. A technique for computing representative profiles of the community members electricity consumption is also presented. The proposed techniques are tested and deployed operationally on a pilot Renewable Energy Community established on an Medium Voltage network in Belgium, involving 2.25MW of wind and 18 Small and Medium Enterprises who had the possibility to freely access the results of the developed data modules by connecting to a dedicated web platform. We first show that our method for dealing with abnormal wind power data improves the forecasting accuracy by 10% in terms of Root Mean Square Error. The impact of the developed data modules on the consumption behaviour of the community members is then quantified, by analyzing the evolution of their monthly self-consumption and self-sufficiency during the pilot. No significant changes in the members behaviour, in relation with the information provided by the models, were observed in the recorded data. The pilot was however perturbed by the COVID-19 crisis which had a significant impact on the economic activity of the involved companies. We conclude by providing recommendations for the future set up of similar communities.
Highlights
The operation and planning of modern electric power systems face major transformations nowadays, due to the increasing share of renewable generation in the electricity mix, which is uncertain by nature and tends to be deployed in a decentralized way, and to the liberalization and unbundling of the electricity supply chain which occurred in the 1990s in Europe.The main challenge with electricity systems consists in the fact that generation and consumption must be physically equal at every instant in order to maintain system stability, since electrical energy is as for difficultly storable at a large scale
We focus on the day-ahead forecasting of time series of local wind power generation in a community, whereas most of the literature studies communities with solar generation only, and on the modeling of the electricity consumption of the individual community members, whereas most of references focus on the consumption quantities aggregated at the community level
18 MW of wind power generation, as well as 70 kW of peak photovoltaic generation, owned by third-party investors
Summary
The operation and planning of modern electric power systems face major transformations nowadays, due to the increasing share of renewable generation (e.g., wind or solar) in the electricity mix, which is uncertain by nature and tends to be deployed in a decentralized way, and to the liberalization and unbundling of the electricity supply chain which occurred in the 1990s in Europe.The main challenge with electricity systems consists in the fact that generation and consumption must be physically equal at every instant in order to maintain system stability, since electrical energy is as for difficultly storable at a large scale. The operation and planning of modern electric power systems face major transformations nowadays, due to the increasing share of renewable generation (e.g., wind or solar) in the electricity mix, which is uncertain by nature and tends to be deployed in a decentralized way, and to the liberalization and unbundling of the electricity supply chain which occurred in the 1990s in Europe. Extreme problems of coordination must thereby be solved by modern Transmission System Operators (TSOs), which are complicated by the fact that they do not own the generation (and consumption) assets, since the liberalization of the electricity sector. The wholesale market level allows in that way interactions between large producers, large consumers and entities known as Access Responsible Parties (ARPs) The retail market enables on the other hand interactions between small end-users (consumers and prosumers) and electricity suppliers (through e.g., fix and varying tariff contracts), which are often themselves ARPs/BRPs
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