Demand Response Analogues for Residential Loads in Natural Gas Networks

Hu, Furong; Sundar, Kaarthik; Srinivasan, Shriram; Bent, Russell

doi:10.1109/access.2021.3132614

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…Figure. 7 shows the wind and solar power data generated by the original and C-DCGAN models. Compared with the original wind and solar power data shown in subgraphs (a) and (c), the wind and solar power data generated by the C-DCGAN model can well depict the volatility of renewable energy output, and the generated data set is consistent with the original data in trend.…”

Section: A Analysis Of the Scheduling Resultsmentioning

confidence: 99%

“…It should be noted that constraint (7) is used to ensure that the schedulable task j at time t is only in one operating condition. (4) Worker quantity constraints max ,, 0…”

Section: Mathematical Model Of Manufacturing Loadmentioning

confidence: 99%

“…In [6], by guiding the DR of electric vehicles, system operators minimize the peak power demand and improve the safe operation level of the power grid. In [7], researchers focused on small capacity residential load, and develops a mathematical formulation to support a residential-level demand response analogue.…”

Section: Introductionmentioning

confidence: 99%

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Day-Ahead Intelligent Energy Management Strategy for Manufacturing Load Participating in Demand Response

Zhang

Zuo

2023

IEEE Access

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Flexible resources such as adjustable load widely participate in interaction with power grid, which can effectively promote renewable energy consumption. In previous studies, researchers generally focused on industrial and household users, but usually ignored the manufacturing load. Therefore, in this paper, an day-ahead intelligent energy management strategy for manufacturing load is proposed. Firstly, we analyze the power demand behavior of manufacturing load in detail, and describe the energy flow and material flow of manufacturing load through state task network (STN) method and mixed integer linear programming model. Then, the conditional deep convolution generative adversarial networks (C-DCGAN) algorithm is used to describe the uncertainty of new energy and construct a set of scheduling scenarios. Finally, case study shows that the proposed method can effectively improve the regional renewable energy consumption level and economic benefits of enterprises. INDEX TERMSManufacturing load; state task network (STN) method; conditional deep convolution generative adversarial networks (C-DCGAN) algorithm; typical scenario screening method

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Section: A Analysis Of the Scheduling Resultsmentioning

confidence: 99%

“…It should be noted that constraint (7) is used to ensure that the schedulable task j at time t is only in one operating condition. (4) Worker quantity constraints max ,, 0…”

Section: Mathematical Model Of Manufacturing Loadmentioning

confidence: 99%

See 1 more Smart Citation

Day-Ahead Intelligent Energy Management Strategy for Manufacturing Load Participating in Demand Response

Zhang

Zuo

2023

IEEE Access

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“…In terms of demand response, in [7], a top-down Hierarchical Optimization (HO) model was proposed to optimise the cost of energy purchase and improve the efficiency of energy use. Hu et al [8] introduce a Heuristics-Based Demand Response (HDR) model for residential-level energy use dispatch, which helps to reduce the peak-to-valley difference in the grid. In general, with the development of new power systems, there is a need to coordinate and interconnect the energy production side and the consumer side to achieve mutual benefits.…”

Section: Introductionmentioning

confidence: 99%

Multi-energy Complementary Power System Economic Dispatch Considering Carbon Emission and Regional User Coordination

Li,

Yang,

Liu

2023

J. Phys.: Conf. Ser.

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The integration of multi-energy complementarity and source-grid-load-storage is an important initiative to promote energy transformation and the high-quality development of power systems. This paper proposes a two-tier day-ahead multi-energy complementary power system economic dispatch model from the perspective of clean and low-carbon, taking into account carbon emissions and multi-regional user synergy. The model optimises the production and purchase of electricity through different decisions between the upper generation side and the lower user side, which minimises the energy production cost in the upper tier and the energy purchase cost in the lower tier, and achieves mutual benefits between the upper and lower tiers. This paper validates the algorithm and model with an improved IEEE 39-node system.

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Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility

et al. 2023

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Demand Response Analogues for Residential Loads in Natural Gas Networks

Cited by 5 publications

References 33 publications

Day-Ahead Intelligent Energy Management Strategy for Manufacturing Load Participating in Demand Response

Day-Ahead Intelligent Energy Management Strategy for Manufacturing Load Participating in Demand Response

Multi-energy Complementary Power System Economic Dispatch Considering Carbon Emission and Regional User Coordination

Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility

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