Multi‐stage flexible planning of regional electricity‐HCNG‐integrated energy system considering gas pipeline retrofit and expansion

Qiu, Yue; Zhou, Suyang; Gu, Wei; Ding, Shixing; Han, Gaoyan; Zhang, Kang; Lv, Hongkun

doi:10.1049/rpg2.12586

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Based on existing research, the uncertainty in local IES can be categorized into three aspects [5][6][7]: 1) uncertain technical parameters, 2) uncertain economic parameters, and 3) extreme disasters and pandemics. Typical uncertainty of technical parameters includes the fluctuation of renewable energy output and variability of multi-energy demand, while the uncertainty of economic parameters usually refers to technical costs and energy prices.…”

Section: Literature Reviewmentioning

confidence: 99%

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Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm

Qiu

Zhou

Dong³

et al. 2023

IET Renewable Power Gen

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Local integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

“…where Vt chp,NG and Vt chp,H are the flow rate of natural gas and hydrogen injected into CHP at time t, respectively, Nm 3 /h. It should be noted that To ensure the safe and stable operation of natural gas infrastructure, such as transmission pipelines and gas appliances, rt hcng needs to meet the constraint in (6).…”

Section: Combined Heating and Power Unitmentioning

confidence: 99%

Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm

Qiu

Zhou

Dong³

et al. 2023

IET Renewable Power Gen

Self Cite

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show abstract

“…For example [23] proposes a cycling decay learning rate deep deterministic policy gradient (CDLR-DDPG) to solve the economical optimization problem in IES to consider the randomness of wind power and the flexibility of upper-level energy prices. An improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed in [24] with a dynamic balance mechanism of exploration noise. The decision-making in the real world is complex, and it may no longer be sufficient to rely solely on a single type of action.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based two‐timescale energy management for energy hub

Chen,

Mao,

Sha

et al. 2024

IET Renewable Power Gen

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Maintaining energy balance and economical operation is significant for energy hub (EH) which serves as the central component. Implementing real‐time regulation for heating and cooling equipment within the EH is challenging due to their slow response time in response to the stochastic fluctuation in renewable energy sources and demands while the opposite is true for electric energy storage equipment (EST), a conventional single timescale energy management strategy is no longer sufficient to take into account the operating characteristics of all equipment. With this motivation, this study proposes a deep reinforcement learning based two‐timescale energy management strategy for EH, which controls heating & cooling equipment on a long timescale of 1 h, and EST on a short timescale of 15 min. The actions of the EST are modelled as discrete to reduce the action spaces, and the discrete‐continuous hybrid action sequential TD3 model is proposed to address the problem of handling both discrete and continuous actions in long timescale policy. A joint training approach based on the centralized training framework is proposed to learn multiple levels of policies in parallel. The case studies demonstrate that the proposed strategy reduces the economic cost and carbon emissions by 1%, and 0.5% compared to the single time‐scale strategy respectively.

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“…Jia et al (2021) initially proposed the definition of the safe operating region of a thermoelectric integrated energy system under the exact AC current model and non-linear heating equation in order to facilitate the safety assessment of the integrated energy system. Yue et al (2022) proposed a multi-stage flexible planning method for regional electrical-thermal integration that takes into account the medium-and long-term dynamic characteristics. Most of the above research is aimed at assessing the flexibility of integrated energy systems, and the existing studies mainly reflect the flexibility of integrated energy systems at a particular scale, and it is difficult to quantitatively assess the flexibility at several different timescales at the same time.…”

Section: Introductionmentioning

confidence: 99%

Multi-timescale available flexibility assessment of integrated energy systems considering different proportions of wind power installations

Zhang,

2024

Front. Energy Res.

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The available flexibility capacity of the integrated energy system can be used as one of the indicators of proportions of system wind power installations, which, in turn, affects the maximum installed capacity of the system wind power, and this paper proposes a method for assessing the available flexibility of the integrated energy system at multiple timescales considering different proportions of system wind power installations. First, the framework of the integrated energy system is constructed, and based on the coupling relationship between the electrical and thermal systems, the mathematical models of the P2G, combined heat and power (CHP), energy storage equipment, and wind power generation equipment within the integrated energy system are established, and the Monte Carlo method is used to predict the wind power output in a typical scenario. Second, an integrated energy system optimization model is constructed to obtain the optimal dispatch operation of the system; the empirical mode decomposition (EMD) algorithm is used to decompose the flexibility demand curve of the system in multiple timescales. The flexibility supply capacity model of different types of flexibility resources in the system at different timescales is established, and through the comparative analysis of flexibility supply and demand at the same timescale, the upward and downward flexibility shortage probability and shortage expectation indexes at each timescale can be intuitively calculated and then weighted to constitute a comprehensive index of system flexibility assessment. Finally, the available flexibility analysis of the integrated energy system under different installed wind power capacities shows that the proposed methodology can more comprehensively assess the available flexibility capacity of the integrated energy system under different timescales, and the maximum installed wind power capacity that the system can withstand can be obtained while guaranteeing sufficient available flexibility capacity.

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Multi‐stage flexible planning of regional electricity‐HCNG‐integrated energy system considering gas pipeline retrofit and expansion

Cited by 14 publications

References 38 publications

Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm

Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm

Reinforcement learning based two‐timescale energy management for energy hub

Multi-timescale available flexibility assessment of integrated energy systems considering different proportions of wind power installations

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