Financial Benefit Analysis of an Electric Water Heater with Direct Load Control in Demand Response

The peak demand reduction during peak hour is a challenge to the retail energy providers. Demand response program plays a major role to fulfil this purpose. The small household appliances like electric water heater can participate in the demand response program by aggregating it in the smart building energy management system. This paper discusses demand response possibilities of a residential electric water heater, the overall consumption profile, temperature profile and the financial benefit in the consumer level. The direct load control demand response method in yearly timeframe is proposed and applied. Realtime electricity pricing with incentive-based demand response is considered and applied to the direct load control with financial benefit to the consumers. The study includes the difference between normal consumption and consumption after using DLC, normal temperature profile and temperature profiling after DLC. The results exhibit that there is significant energy consumption reduction in the consumer level without making any discomfort.The present work was done and funded in the scope of the following projects: H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); SIMOCE (ANI|P2020 17690); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Asignación de recursos distribuidos para una electrolinera basado en el algoritmo colonia artificial de hormigas

En el presente documento se expone el estudio del impacto producido por la inserción de nuevas cargas a la red, para este caso la cantidad de vehículos eléctricos que ingresan en una electrolinera. Se utilizo un modelo heurístico basado en el algoritmo colonia artificial de hormigas, el cual se basa en la forma en que este insecto recolecta su alimento utilizando siempre el camino más corto, lo que permitió la óptima asignación de recursos distribuidos para la carga de vehículos eléctricos, empleando un programa de respuesta de la demanda denominado pico crítico. Para el estudio se modelo la electrolinera y la cantidad de vehículos que ingresan en el lapso de un día durante cada hora. Para el análisis de resultados se tomó en cuenta dos casos; invierno y verano. Para continuar con la investigación se utilizó recursos como panales fotovoltaicos y banco de baterías como fuente de generación distribuida. La solución se obtuvo en base a programación lineal implementando el algoritmo en el software Matlab para obtener la mejor respuesta.This paper presents the study of the impact produced by the insertion of new loads to the grid, in this case the number of electric vehicles entering an electric station. A heuristic model based on the artificial ant colony algorithm was used, which is based on the way this insect collects its food always using the shortest path, which allowed the optimal allocation of distributed resources for charging electric vehicles, using a demand response program called critical load. For the study, we modeled the electric station and the number of vehicles entering the station in the period of one day during each hour. For the analysis of the results, two cases were considered; winter and summer. To continue with the investigation, resources such as photovoltaic panels and battery bank were used as a source of distributed generation. The solution was obtained based on linear programming by implementing the algorithm in Matlab software to obtain the optimal answer

Financial Benefit Analysis of an Electric Water Heater with Direct Load Control in Demand Response

The peak demand reduction during peak hour is a challenge to the retail energy providers. Demand response program plays a major role to fulfil this purpose. The small household appliances like electric water heater can participate in the demand response program by aggregating it in the smart building energy management system. This paper discusses demand response possibilities of a residential electric water heater, the overall consumption profile, temperature profile and the financial benefit in the consumer level. The direct load control demand response method in yearly timeframe is proposed and applied. Realtime electricity pricing with incentive-based demand response is considered and applied to the direct load control with financial benefit to the consumers. The study includes the difference between normal consumption and consumption after using DLC, normal temperature profile and temperature profiling after DLC. The results exhibit that there is significant energy consumption reduction in the consumer level without making any discomfort.The present work was done and funded in the scope of the following projects: H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); SIMOCE (ANI|P2020 17690); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Deep Reinforcement Learning Based Approach for Multi-Agent Control of Residential Electric Water Heaters for Distribution Load Management

The push towards decarbonization and electrification of the society is leading to increased electricity demand. Many countries, including Canada, are utilizing non-greenhouse gas (GHG) emitting sources and renewable energy sources (RES) to meet this increasing demand. Many of the RES, however, are intermittent and uncertain, and are non-load following sources of electricity. Technologies supporting demand flexibility are being increasingly used to respond to intermittent changes in RES supply and meet the power grid requirements by modifying the energy consumption patterns of residential loads. The work presented in this thesis discusses the application of electric water heaters (EWHs) as flexible and controllable loads. EWHs, accounting for a significant portion (44%) of water heaters in the Canadian residential sector, and being the second largest consumer of electricity in the household sector (20%), are becoming a viable source for providing load flexibility. This thesis presents a multi-agent reinforcement learning (MARL) approach to address the energy management problem of EWHs. Two agents, the residential aggregator agent (RAA)- for EWH control and the utility agent (UA)- to represent the role of a utility, are designed to interact with each other and the (reinforcement learning) environment to maximize their respective rewards. A novel control algorithm using a binning process is employed by the RAA to control operations of certain groups of EWHs. The multi-agent deep deterministic policy gradient (MADDPG) algorithm is implemented for this problem and used in training the RAA and UA to follow the optimal policy. The proposed EWH energy management approach is tested for consumers in Ontario, New Brunswick and Quebec which have varying consumer tariff rates. The results demonstrate the ability of the proposed RAA and UA to control the behaviour of EWHs via price incentive signals, thus providing benefits for the consumers and the utility