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A novel demand response-based distributed multi-energy system optimal operation framework for data centers
https://orcid.org/0000-0002-6706-7168
Graphical abstracts Display Omitted
Highlights A DMES optimization model to boost renewable energy usage in data center is proposed. A demand response model is built considered electricity price and renewable energy. Monte Carlo and interval methods are combined to analyze effects of multi-uncertainty. Model reduces costs by 1.85% but boosts renewable energy utilization by 0.77%. Multi-uncertainty optimization increased DMES’s costs but reduced operation risk.
Abstract To address the increasing energy demands in data centers, distributed multi-energy system (DMES) can provide an efficient solution. This study proposes a DMES coupled by photovoltaics, wind turbines, ground source heat pump, internal combustion engines, electric chillers, absorption chillers, and energy storage device. The optimal scheduling of DMES with source-load interaction plays a crucial role in improving the performance of data centers. To achieve this, an integrated demand response (IDR) strategy is established, considering time-of-use pricing and renewable energy peak subsidies. Then, a clean and economical optimal operation model for DMES based on proposed IDR strategy and carbon trading mechanisms is established. Furthermore, to enhance the robustness of scheduling, this study analyzes the impacts of uncertainties in renewable energy output and demand response on DMES operation using the Monte Carlo method and interval method. Case study results demonstrate that the proposed IDR strategy increases electricity and cooling loads while reducing heat loads during the peak period of renewable energy generation, compared to the traditional IDR strategy. The proposed scheduling model, which incorporates the proposed IDR strategy and carbon trading mechanism, improves data center performance by reducing total costs by 1.85%, carbon emissions by 0.56%, electricity purchases by 2.93%, while increasing renewable energy utilization by 0.77%. The results also highlight the critical role of energy storage devices, with an optimal initial charge state is 0.5. Additionally, optimization considering multiple uncertainties, results in higher total costs and broader interval widths compared to single-factor uncertainty optimization, but reduces system operational risk.
A novel demand response-based distributed multi-energy system optimal operation framework for data centers
https://orcid.org/0000-0002-6706-7168
Graphical abstracts Display Omitted
Highlights A DMES optimization model to boost renewable energy usage in data center is proposed. A demand response model is built considered electricity price and renewable energy. Monte Carlo and interval methods are combined to analyze effects of multi-uncertainty. Model reduces costs by 1.85% but boosts renewable energy utilization by 0.77%. Multi-uncertainty optimization increased DMES’s costs but reduced operation risk.
Abstract To address the increasing energy demands in data centers, distributed multi-energy system (DMES) can provide an efficient solution. This study proposes a DMES coupled by photovoltaics, wind turbines, ground source heat pump, internal combustion engines, electric chillers, absorption chillers, and energy storage device. The optimal scheduling of DMES with source-load interaction plays a crucial role in improving the performance of data centers. To achieve this, an integrated demand response (IDR) strategy is established, considering time-of-use pricing and renewable energy peak subsidies. Then, a clean and economical optimal operation model for DMES based on proposed IDR strategy and carbon trading mechanisms is established. Furthermore, to enhance the robustness of scheduling, this study analyzes the impacts of uncertainties in renewable energy output and demand response on DMES operation using the Monte Carlo method and interval method. Case study results demonstrate that the proposed IDR strategy increases electricity and cooling loads while reducing heat loads during the peak period of renewable energy generation, compared to the traditional IDR strategy. The proposed scheduling model, which incorporates the proposed IDR strategy and carbon trading mechanism, improves data center performance by reducing total costs by 1.85%, carbon emissions by 0.56%, electricity purchases by 2.93%, while increasing renewable energy utilization by 0.77%. The results also highlight the critical role of energy storage devices, with an optimal initial charge state is 0.5. Additionally, optimization considering multiple uncertainties, results in higher total costs and broader interval widths compared to single-factor uncertainty optimization, but reduces system operational risk.
A novel demand response-based distributed multi-energy system optimal operation framework for data centers
https://orcid.org/0000-0002-6706-7168
Graphical abstracts Display Omitted
Highlights A DMES optimization model to boost renewable energy usage in data center is proposed. A demand response model is built considered electricity price and renewable energy. Monte Carlo and interval methods are combined to analyze effects of multi-uncertainty. Model reduces costs by 1.85% but boosts renewable energy utilization by 0.77%. Multi-uncertainty optimization increased DMES’s costs but reduced operation risk.
Abstract To address the increasing energy demands in data centers, distributed multi-energy system (DMES) can provide an efficient solution. This study proposes a DMES coupled by photovoltaics, wind turbines, ground source heat pump, internal combustion engines, electric chillers, absorption chillers, and energy storage device. The optimal scheduling of DMES with source-load interaction plays a crucial role in improving the performance of data centers. To achieve this, an integrated demand response (IDR) strategy is established, considering time-of-use pricing and renewable energy peak subsidies. Then, a clean and economical optimal operation model for DMES based on proposed IDR strategy and carbon trading mechanisms is established. Furthermore, to enhance the robustness of scheduling, this study analyzes the impacts of uncertainties in renewable energy output and demand response on DMES operation using the Monte Carlo method and interval method. Case study results demonstrate that the proposed IDR strategy increases electricity and cooling loads while reducing heat loads during the peak period of renewable energy generation, compared to the traditional IDR strategy. The proposed scheduling model, which incorporates the proposed IDR strategy and carbon trading mechanism, improves data center performance by reducing total costs by 1.85%, carbon emissions by 0.56%, electricity purchases by 2.93%, while increasing renewable energy utilization by 0.77%. The results also highlight the critical role of energy storage devices, with an optimal initial charge state is 0.5. Additionally, optimization considering multiple uncertainties, results in higher total costs and broader interval widths compared to single-factor uncertainty optimization, but reduces system operational risk.
A novel demand response-based distributed multi-energy system optimal operation framework for data centers
Ren, Xiaoxiao (author) / Wang, Jinshi (author) / Hu, Xiaoyang (author) / Sun, Zhiyong (author) / Zhao, Quanbin (author) / Chong, Daotong (author) / Xue, Kai (author) / Yan, Junjie (author)
Energy and Buildings ; 305
2024-01-02
Article (Journal)
Electronic Resource
English
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