Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control

Alimohammadisagvand, Behrang; Jokisalo, Juha; Kilpeläinen, Simo; Ali, Mubbashir; Sirén, Kai

doi:10.1016/j.apenergy.2016.04.013

Cited by 127 publications

(57 citation statements)

References 31 publications

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“…To solve this problem, various studies are being conducted on the ESS as a back-up system that can store the electricity generated from the RE. In this study, the previous studies related to ESS are classified into those on the thermal ESS and those on the electrical ESS depending on the type of stored energy (refer to Table 12) [136][137][138][139][140][141][142]. First, the previous studies conducted in terms of thermal ESS are as follows [136][137][138][139].…”

Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

“…and the step of substituting the residual building energy demand through energy supply from active technologies (e.g., PV system with ESS) must be adopted. Most of the previous studies, however, were conducted by focusing on only one strategy (i.e., passive or active strategies) [130][131][132][133][134][135][136][137][138][139][140][141][142]. Therefore, an integrated analysis of the energy performance of a building where both passive and active strategies are applied should be performed in the early phase of a building's life cycle from the perspective of achieving nZEB.…”

Section: Integration Of the Passive And Active Strategiesmentioning

confidence: 99%

“…In this study, the previous studies related to ESS are classified into those on the thermal ESS and those on the electrical ESS depending on the type of stored energy (refer to Table 12) [136][137][138][139][140][141][142]. First, the previous studies conducted in terms of thermal ESS are as follows [136][137][138][139]. Alimohammadisagvand et al (2016) aimed to find a cost-optimal solution for thermal ESS integrated with the geothermal system for a residential building in a cold climate based on the concept of demand response (DR) (i.e., a momentary DR control based on the real-time hourly electricity price, a backwards-looking DR control based on the previous hourly electricity price, and a predictive DR control based on future hourly electricity price).…”

Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

“…First, the previous studies conducted in terms of thermal ESS are as follows [136][137][138][139]. Alimohammadisagvand et al (2016) aimed to find a cost-optimal solution for thermal ESS integrated with the geothermal system for a residential building in a cold climate based on the concept of demand response (DR) (i.e., a momentary DR control based on the real-time hourly electricity price, a backwards-looking DR control based on the previous hourly electricity price, and a predictive DR control based on future hourly electricity price). The analysis result showed that applying the predictive DR control algorithm is most effective in terms of the annual savings in the total delivered energy and cost [136].…”

Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

“…Alimohammadisagvand et al (2016) aimed to find a cost-optimal solution for thermal ESS integrated with the geothermal system for a residential building in a cold climate based on the concept of demand response (DR) (i.e., a momentary DR control based on the real-time hourly electricity price, a backwards-looking DR control based on the previous hourly electricity price, and a predictive DR control based on future hourly electricity price). The analysis result showed that applying the predictive DR control algorithm is most effective in terms of the annual savings in the total delivered energy and cost [136]. Al Zahrani and Dincer (2016) conducted a performance evaluation of the aquifer thermal ESS considering the charging temperature, storing time, temperature during storing, discharging temperature, etc.…”

Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

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Advanced Strategies for Net-Zero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle

Hong

Kim

et al. 2017

Sustainability

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Abstract:To cope with 'Post-2020', each country set its national greenhouse gas (GHG) emissions reduction target (e.g., South Korea: 37%) below its business-as-usual level by 2030. Toward this end, it is necessary to implement the net-zero energy building (nZEB) in the building sector, which accounts for more than 25% of the national GHG emissions and has a great potential to reduce GHG emissions. In this context, this study conducted a state-of-the-art review of nZEB implementation strategies in terms of passive strategies (i.e., passive sustainable design and energy-saving technique) and active strategies (i.e., renewable energy (RE) and back-up system for RE). Additionally, this study proposed the following advanced strategies for nZEB implementation according to a building's life cycle: (i) integration and optimization of the passive and active strategies in the early phase of a building's life cycle; (ii) real-time monitoring of the energy performance during the usage phase of a building's life cycle. It is expected that this study can help researchers, practitioners, and policymakers understand the overall implementation strategies for realizing nZEB.

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Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

Section: Integration Of the Passive And Active Strategiesmentioning

confidence: 99%

Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

Section: Part B-2: Back-up Systems For Rementioning

confidence: 99%

See 3 more Smart Citations

Advanced Strategies for Net-Zero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle

Hong

Kim

et al. 2017

Sustainability

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Demand Response in Smart Buildings

Prusty

Arun

Falco

2022

Control of Smart Buildings

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Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

Wicaksono,

Trat,

Bashyal

et al. 2024

Int J Adv Manuf Technol

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The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k-nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

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Cost-optimal thermal energy storage system for a residential building with heat pump heating and demand response control

Cited by 127 publications

References 31 publications

Advanced Strategies for Net-Zero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle

Advanced Strategies for Net-Zero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle

Demand Response in Smart Buildings

Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

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