Consumption–Production Profile Categorization in Energy Communities
Energy Transition is changing the renewable energy participation in new distributed generation systems like the Local Energy Markets. Due to its inherent intermittent and variable nature, forecasting production and consumption load profiles will be more challenging and demand more complex predictive...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/19/6996 |
| _version_ | 1797575919114649600 |
|---|---|
| author | Wolfram Rozas Rafael Pastor-Vargas Angel Miguel García-Vico José Carpio |
| author_facet | Wolfram Rozas Rafael Pastor-Vargas Angel Miguel García-Vico José Carpio |
| author_sort | Wolfram Rozas |
| collection | DOAJ |
| description | Energy Transition is changing the renewable energy participation in new distributed generation systems like the Local Energy Markets. Due to its inherent intermittent and variable nature, forecasting production and consumption load profiles will be more challenging and demand more complex predictive models. This paper analyzes the production, consumption load profile, and storage headroom% of the Cornwall Local Energy Market, using advanced statistical time series methods to optimize the opportunity market the storage units provide. These models also help the Energy Community storage reserves to meet contract conditions with the Distribution Network Operator. With this more accurate and detailed knowledge, all sites from this Local Energy Market will benefit more from their installation by optimizing their energy consumption, production, and storage. This better accuracy will make the Local Energy Market more fluid and safer, creating a flexible system that will guarantee the technical quality of the product for the whole community. The training of several SARIMAX, Exponential Smoothing, and Temporal Causal models improved the fitness of consumption, production, and headroom% time series. These models properly decomposed the time series in trend, seasonality, and stochastic dynamic components that help us to understand how the Local Energy Market consumes, produces, and stores energy. The model design used all power flows and battery energy storage system state-of-charge site characteristics at daily and hourly granularity levels. All model building follows an analytical methodology detailed step by step. A benchmark between these sequence models and the incumbent forecasting models utilized by the Energy Community shows a better performance measured with model error reduction. The best models present mean squared error reduction between 88.89% and 99.93%, while the mean absolute error reduction goes from 65.73% to 97.08%. These predictive models built at different prediction scales will help the Energy Communities better contribute to the Network Management and optimize their energy and power management performance. In conclusion, the expected outcome of these implementations is a cost-optimal management of the Local Energy Market and its contribution to the needed new Flexibility Electricity System Scheme, extending the adoption of renewable energies. |
| first_indexed | 2024-03-10T21:46:08Z |
| format | Article |
| id | doaj.art-d8ecbfaaf8954d398ba19f67443d5343 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T21:46:08Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d8ecbfaaf8954d398ba19f67443d53432023-11-19T14:21:46ZengMDPI AGEnergies1996-10732023-10-011619699610.3390/en16196996Consumption–Production Profile Categorization in Energy CommunitiesWolfram Rozas0Rafael Pastor-Vargas1Angel Miguel García-Vico2José Carpio3Departamento de Sistemas de Comunicación y Control, Escuela Técnica Superior en Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainDepartamento de Sistemas de Comunicación y Control, Escuela Técnica Superior en Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainDepartamento de Sistemas de Comunicación y Control, Escuela Técnica Superior en Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainEscuela Técnica Superior de Ingenieros Industriales, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, SpainEnergy Transition is changing the renewable energy participation in new distributed generation systems like the Local Energy Markets. Due to its inherent intermittent and variable nature, forecasting production and consumption load profiles will be more challenging and demand more complex predictive models. This paper analyzes the production, consumption load profile, and storage headroom% of the Cornwall Local Energy Market, using advanced statistical time series methods to optimize the opportunity market the storage units provide. These models also help the Energy Community storage reserves to meet contract conditions with the Distribution Network Operator. With this more accurate and detailed knowledge, all sites from this Local Energy Market will benefit more from their installation by optimizing their energy consumption, production, and storage. This better accuracy will make the Local Energy Market more fluid and safer, creating a flexible system that will guarantee the technical quality of the product for the whole community. The training of several SARIMAX, Exponential Smoothing, and Temporal Causal models improved the fitness of consumption, production, and headroom% time series. These models properly decomposed the time series in trend, seasonality, and stochastic dynamic components that help us to understand how the Local Energy Market consumes, produces, and stores energy. The model design used all power flows and battery energy storage system state-of-charge site characteristics at daily and hourly granularity levels. All model building follows an analytical methodology detailed step by step. A benchmark between these sequence models and the incumbent forecasting models utilized by the Energy Community shows a better performance measured with model error reduction. The best models present mean squared error reduction between 88.89% and 99.93%, while the mean absolute error reduction goes from 65.73% to 97.08%. These predictive models built at different prediction scales will help the Energy Communities better contribute to the Network Management and optimize their energy and power management performance. In conclusion, the expected outcome of these implementations is a cost-optimal management of the Local Energy Market and its contribution to the needed new Flexibility Electricity System Scheme, extending the adoption of renewable energies.https://www.mdpi.com/1996-1073/16/19/6996flexibilitylocal energy marketpredictive sequence modelsuncertainty |
| spellingShingle | Wolfram Rozas Rafael Pastor-Vargas Angel Miguel García-Vico José Carpio Consumption–Production Profile Categorization in Energy Communities Energies flexibility local energy market predictive sequence models uncertainty |
| title | Consumption–Production Profile Categorization in Energy Communities |
| title_full | Consumption–Production Profile Categorization in Energy Communities |
| title_fullStr | Consumption–Production Profile Categorization in Energy Communities |
| title_full_unstemmed | Consumption–Production Profile Categorization in Energy Communities |
| title_short | Consumption–Production Profile Categorization in Energy Communities |
| title_sort | consumption production profile categorization in energy communities |
| topic | flexibility local energy market predictive sequence models uncertainty |
| url | https://www.mdpi.com/1996-1073/16/19/6996 |
| work_keys_str_mv | AT wolframrozas consumptionproductionprofilecategorizationinenergycommunities AT rafaelpastorvargas consumptionproductionprofilecategorizationinenergycommunities AT angelmiguelgarciavico consumptionproductionprofilecategorizationinenergycommunities AT josecarpio consumptionproductionprofilecategorizationinenergycommunities |