A Novel Evolving Framework for Energy Management in Combined Heat and Electricity Systems with Demand Response Programs

Cui, Ting; Gan, Lei; Iqbal, Sheeraz; Jasiński, Marek; El-Meligy, Mohammed A.; Sharaf, Mohamed; Ali, Samia Gharib

doi:10.3390/su151310481

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…In order to fully adapt to renewable energy and cope with the imbalance between supply and demand, previous studies have mainly focused on demand response (DR) or energy storage systems. The DR concept covers any intentional modification of the user's use of electricity [26,27]. The DR can be achieved through load regulation and control, and is critical to improving performance and security [28].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response

Zhang,

Lan,

2023

Sustainability

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To enhance the low-carbon level and economic performance of microgrid systems while considering the impact of renewable energy output uncertainty on system operation stability, this paper presents a robust optimization microgrid model based on carbon-trading mechanisms and demand–response mechanisms. Regarding the carbon-trading mechanism, the baseline allocation method is utilized to provide carbon emission quotas to the system at no cost, and a ladder carbon price model is implemented to construct a carbon transaction cost model. Regarding uncertainty set construction, the correlation of distributed generation in time and space is considered, and a new uncertainty set is constructed based on historical data to reduce the conservative type of robust optimization. Based on the column constraint generation algorithm, the model is solved. The findings indicate that upon considering the carbon-trading mechanism, the microgrid tends to increase the output of low-carbon units and renewable energy units, and the carbon emissions of the microgrid can be effectively reduced. However, due to the increase in power purchase from the distribution network and the increase in carbon transaction costs, the operating costs of the microgrid increase. Secondly, through the utilization of demand–response mechanisms, the microgrid can achieve load transfer between peaks and troughs. It is imperative to establish appropriate compensation costs for demand and response that balances both economic efficiency and system stability. At the same time, due to the time-of-use electricity price, the energy storage equipment can also play a load transfer effect and improve the system’s economy. Finally, sensitivity analysis was conducted on the adjustment parameters of distributed power sources and loads that have uncertain values. A comparison was made between the deterministic scheduling model and the two-stage robust optimization model proposed in this study. It was proved that this model has great advantages in coordinating the economy, stability and low carbon level of microgrid operations.

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Section: Introductionmentioning

confidence: 99%

A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response

Zhang,

Lan,

2023

Sustainability

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Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets

Parhoudeh,

Eguía López,

Kavousi Fard

2024

Smart Cities

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An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction.

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A Novel Evolving Framework for Energy Management in Combined Heat and Electricity Systems with Demand Response Programs

Cited by 2 publications

References 29 publications

A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response

A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response

Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets

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