<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Energy is an important basic resource for the development of human society. This paper develops a decision support system for collaborative optimization of biomass energy and intelligence using biomass energy and smart energy management technology. Explain the various methods by which biomass can be transformed into usable energy for power plants. A smart energy management model is constructed using Internet of Things technology, and its performance prediction model is predicted using the gray prediction model. To study the optimal allocation of energy in the system constructed in this paper, the objective function and constraints are built using carbon capture technology, and the optimal allocation conditions of energy are calculated. After experimental analysis, it was found that in 2020, biomass energy power generation reached 29 million kilowatts, which was 020.1 million kilowatts higher than in 2015. The co-optimization strategy of biomass energy and smart energy constructed in this paper supports the energy utilization efficiency of the system to reach more than 60%. Compared to the traditional energy division system, the constructed system saves an annual cost of 52.71%. The payback period was shortened to 3.31 years, resulting in a net present value increase of 2.8991 million yuan. The energy synergy system’s power supply produces the least amount of carbon dioxide from an environmental perspective and has been stabilized at 50 kg.</p>
</abstract>

Energy is an important basic resource for the development of human society. This paper develops a decision support system for collaborative optimization of biomass energy and intelligence using biomass energy and smart energy management technology. Explain the various methods by which biomass can be transformed into usable energy for power plants. A smart energy management model is constructed using Internet of Things technology, and its performance prediction model is predicted using the gray prediction model. To study the optimal allocation of energy in the system constructed in this paper, the objective function and constraints are built using carbon capture technology, and the optimal allocation conditions of energy are calculated. After experimental analysis, it was found that in 2020, biomass energy power generation reached 29 million kilowatts, which was 020.1 million kilowatts higher than in 2015. The co-optimization strategy of biomass energy and smart energy constructed in this paper supports the energy utilization efficiency of the system to reach more than 60%. Compared to the traditional energy division system, the constructed system saves an annual cost of 52.71%. The payback period was shortened to 3.31 years, resulting in a net present value increase of 2.8991 million yuan. The energy synergy system’s power supply produces the least amount of carbon dioxide from an environmental perspective and has been stabilized at 50 kg.

Collaborative Optimization Decision Support System for Biomass and Smart Energy Based on Virtual Power Plant

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Energy is an important basic resource for the development of human society. This paper develops a decision support system for collaborative optimization of...

Collaborative Optimization Decision Support System for Biomass and Smart Energy Based on Virtual Power Plant

Data publikacji: 03 cze 2024

Zakres stron: -

Otrzymano: 20 lut 2024

Przyjęty: 29 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1336

Słowa kluczowe
Grey prediction model, Carbon capture technology, Constraints, Biomass energy, Collaborative optimization

© 2024 Weirong Wang, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Collaborative Optimization Decision Support System for Biomass and Smart Energy Based on Virtual Power Plant

Data publikacji: 03 cze 2024

Zakres stron: -

Otrzymano: 20 lut 2024

Przyjęty: 29 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1336

Słowa kluczoweGrey prediction model, Carbon capture technology, Constraints, Biomass energy, Collaborative optimization

© 2024 Weirong Wang, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Grey prediction model, Carbon capture technology, Constraints, Biomass energy, Collaborative optimization