Capacity configuration optimization of standalone multi‐energy hub considering electricity, heat and hydrogen uncertainty

Hou, Hui; Liu, Peng; Xiao, Zhifeng; Deng, Xiangtian; Huang, Liang; Zhang, Ruiming; Xie, Changjun

doi:10.1049/enc2.12028

Cited by 27 publications

(15 citation statements)

References 36 publications

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“…Similar to the mechanism of the TGC but targeting the carbon emission, the IES can gain profits if the carbon emission is smaller than the allocated quota, and may need to purchase carbon emission rights if the actual emission is larger than the quota [29], as shown in (14), where ξ em is the price of carbon emission rights, E t D is the carbon emission quota of the system at time t, and M t D is the actual emission.…”

Section: Carbon Emission Rightmentioning

confidence: 99%

“…In [13], a weighting factor is introduced to balance the minimization of grid operation cost and REG curtailment. In [14], the hybrid multi-objective particle swarm optimization (HMOPSO) method is adopted to minimize economic cost, maximize REG utilization, and minimize expected energy not supplied (EENS) at the same time. In [15], the cost of REG curtailment caused by flexibility shortage of conventional generators is included in the objective function.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Time Period Optimal Dispatch Strategy for Integrated Energy System Considering Renewable Energy Generation Accommodation

Chen¹,

Lai

Chen³

et al. 2022

Energies

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With the target of carbon peaking and carbon neutrality, renewable energy generation (REG) develops rapidly. The increasing penetration of REG brings along the problems of fluctuation in power flow and the possible abandonment of wind and photovoltaics (PV) generation. In this context, the so-called integrated energy system (IES) becomes a promising solution to the accommodation of REG thanks to energy storage systems and coupling devices inside. In this paper, the optimal operation model of an IES is first presented, with the schemes of green certificate trading and carbon emission right trading included to provide economic incentives for accommodating REG. Next, in order to address the problem of uncertainty in REG, the devices in the IES are divided into three types based on regulation flexibility, and a multi-time period optimal dispatching scheme is proposed, including day-ahead optimal scheduling, rolling optimal dispatching, and real-time control strategy. Finally, it is demonstrated by simulation results of a numerical example that the proposed method not only promotes the accommodation capability for REG but can also cope well with contingencies.

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Section: Carbon Emission Rightmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Time Period Optimal Dispatch Strategy for Integrated Energy System Considering Renewable Energy Generation Accommodation

Chen¹,

Lai

Chen³

et al. 2022

Energies

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show abstract

“…Many studies have investigated topics related to ERs, such as practical topologies [31][32][33][34], control and energy management strategies [35][36][37][38][39][40][41], power routing algorithms [42][43][44], and test prototypes [45][46][47]. An ER topology with a variable topology was proposed in ref.…”

Section: Introductionmentioning

confidence: 99%

“…An improved multi‐objective particle swarm optimisation (PSO) algorithm and fuzzy membership function algorithm were proposed in ref. [41] to solve a multi‐objective capacity allocation model for independent multiple ERs while considering the uncertainty in electricity, heat, and hydrogen energy. A continuous‐time Markov chain model describing a system architecture based on an ER was proposed in ref.…”

Section: Introductionmentioning

confidence: 99%

Energy router interconnection system: A solution for new distribution network architecture toward future carbon neutrality

Liu

Zhu

Guan

et al. 2022

Energy Conversion and Econom

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Under the background of carbon neutrality, distribution networks are facing many new challenges, including providing higher power supply reliability and power quality, additional power supply forms, and better information sharing. The traditional distribution network has difficulty coping with these challenges; thus, it is imperative to transform the traditional distribution network architecture. An energy router (ER) is a type of intelligent power electronic device, and has the potential to play a great role in the transformation of the distribution network. This paper proposes the basic architecture of an ER interconnection system (ERIS), where multiple ERs are gathered together to play a stronger role. Aiming for two different stages of the transformation process of the distribution network, two types of ERISs are employed for a single prosumer and multiple prosumers, respectively. The equivalent modelling, main control strategies, and energy management schemes of the two types of ERIS are respectively illustrated. Several ERIS simulation cases are investigated, and the results verify the advantages and satisfactory performance of the ERIS. The proposed ERIS provides an effective solution for building a new distribution network to adapt to the new challenges in a future carbon neutral era.

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“…As a typical time series forecasting problem, many STLF methods have been a hot topic in academia and industry (Cai et al, 2017;Hou et al, 2020a;Hou et al, 2020b;Cai et al, 2021;Hou et al, 2021), which can be roughly categorized into statistical methods and artificial intelligence methods (Kuster et al, 2017). Among them, the statistical methods (Zhao and Li, 2021;López et al, 2019) are difficult to handle load time series with high randomness and non-linearity (Wang et al, 2021b) and usually result in low forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Probability Density Function Forecasting of Industrial Loads Based on ConvLSTM-MDN

Wang

Chen

et al. 2022

Front. Energy Res.

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Load forecasting for industrial customers is essential for reliable operation decisions in the electric power industry. However, most of the load forecasting literature has been focused on deterministic load forecasting (DLF) without considering information on the uncertainty of industrial load. This article proposes a probabilistic density load forecasting model comprising convolutional long short-term memory (ConvLSTM) and a mixture density network (MDN). First, a sliding window strategy is adopted to convert one-dimensional (1D) data into two-dimensional (2D) matrices to reconstruct input features. Then the ConvLSTM is utilized to capture the deep information of the input features. At last, the mixture density network capable of directly predicting probability density functions of loads is adopted. Experimental results on the load datasets of three different industries show the accuracy and reliability of the proposed method.

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Capacity configuration optimization of standalone multi‐energy hub considering electricity, heat and hydrogen uncertainty

Cited by 27 publications

References 36 publications

Multi-Time Period Optimal Dispatch Strategy for Integrated Energy System Considering Renewable Energy Generation Accommodation

Multi-Time Period Optimal Dispatch Strategy for Integrated Energy System Considering Renewable Energy Generation Accommodation

Energy router interconnection system: A solution for new distribution network architecture toward future carbon neutrality

Short-Term Probability Density Function Forecasting of Industrial Loads Based on ConvLSTM-MDN

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