Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House
Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/9/1645 |
| _version_ | 1798035341831045120 |
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| author | Ricardo Faia Pedro Faria Zita Vale João Spinola |
| author_facet | Ricardo Faia Pedro Faria Zita Vale João Spinola |
| author_sort | Ricardo Faia |
| collection | DOAJ |
| description | Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost. |
| first_indexed | 2024-04-11T20:56:53Z |
| format | Article |
| id | doaj.art-d0f96dc853ff49aa9d66a8e7c279ff4a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T20:56:53Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d0f96dc853ff49aa9d66a8e7c279ff4a2022-12-22T04:03:40ZengMDPI AGEnergies1996-10732019-04-01129164510.3390/en12091645en12091645Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential HouseRicardo Faia0Pedro Faria1Zita Vale2João Spinola3Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, PortugalPolytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, PortugalPolytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, PortugalPolytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, PortugalDemand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.https://www.mdpi.com/1996-1073/12/9/1645demand responsedistributed generationparticle swarm optimizationprosumer |
| spellingShingle | Ricardo Faia Pedro Faria Zita Vale João Spinola Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House Energies demand response distributed generation particle swarm optimization prosumer |
| title | Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House |
| title_full | Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House |
| title_fullStr | Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House |
| title_full_unstemmed | Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House |
| title_short | Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House |
| title_sort | demand response optimization using particle swarm algorithm considering optimum battery energy storage schedule in a residential house |
| topic | demand response distributed generation particle swarm optimization prosumer |
| url | https://www.mdpi.com/1996-1073/12/9/1645 |
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