Demand response from multiple-energy customers in integrated energy system

Shao, Changzhen; Ding, Yi; Song, Yonghua; Zhu, Chi

doi:10.1109/eeeic.2017.7977663

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…. The coupling between different kinds of energy carriers limits the operational flexibility of the IES, which contradicts with the increasing penetration of variable wind power . Thus, the increasingly mature PtG technology is of relevance when considering the increasing significance of variable wind power, which converts surplus energy into hydrogen or natural gas for storage and utilization.…”

Section: Eh Model Considering Demand Responsementioning

confidence: 99%

Optimal configuration and sizing of regional energy service company's energy hub with integrated demand response

Qiu

Wang

Huang³

et al. 2018

IEEJ Transactions Elec Engng

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The growing development of interactive energy systems has motivated a regional energy service company (ESCO) to build energy hubs (EH) that play a substantial role in the coupling of various energy carriers. EH configuration optimization is critical to the economic operation of an ESCO. Therefore, this study proposes a configuration and sizing model that considers the integrated demand response (IDR) to optimize the size of the infrastructures in an EH, and hence minimize the total cost. The EH, which is composed of converters and storage units, is modelled in the matrix form, wherein the operation constraints are expressed in detail. Subsequently, an IDR program based on both responsive thermal and electrical loads is innovatively incorporated into the optimization model, and its effects on EH planning were investigated. By considering several cases: (i) we confirm the effectiveness of the IDR in terms of both cost and capacity reduction on components in the EH; (ii) we show the limited impact of the IDR on the investment cost and equipment configuration, primarily owing to constraints on the maximal total number of dispatching hours; (iii) we observe variations in the impact degree on infrastructure capacity, based on several factors including load characteristics and the electricity–gas price ratio. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Eh Model Considering Demand Responsementioning

confidence: 99%

Optimal configuration and sizing of regional energy service company's energy hub with integrated demand response

Qiu

Wang

Huang³

et al. 2018

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Literature [7][8][9] starts with assessing the economy and flexibility of system operation through the coupling transformation of electricity, heat, gas and other energy in the IES, optimizes the scheduling of the system over multiple time scales, and finally realizes the flexibility and economic operation of IES. Based on the demand response of the load side, the literature [10][11][12] establish the IES optimization model. Through the scheduling transformation of the energy in the system, the unique characteristics of energy in the system can be load matched so that the economy of the scheduling results can be further improved.…”

Section: Introductionmentioning

confidence: 99%

Research on the optimal scheduling of a multi- storage combined integrated energy system based on an energy supply grading strategy

Zhang,

You,

et al. 2024

Preprint

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As an important supporting technology for carbon neutrality strategy, the combination of an integrated energy system and hydrogen storage is expected to become a key research direction. To address the insufficient flexibility of multi-energy coupling in the integrated energy system and the overall strategic demand of low-carbon development, a multi-storage integrated energy system architecture that includes electric storage, heat storage and hydrogen storage is established. Then, according to the system status factors, such as energy cost, response characteristics and energy storage status, a hierarchical energy supply control strategy focusing on the energy supply priority of energy storage units is proposed. NSGA-Ⅱ-MOABC is created as the optimization algorithm to meet the demand of multi-energy coupling energy supply in the integrated energy system. Next, considering the system operational cost and carbon emission cost as the optimization goal, a comprehensive energy optimization scheduling model of multi-storage combined hierarchical energy supply is constructed. Based on the simulation example, the scheduling results of the multi-storage combined system are obtained, through comparative analysis, the addition of hydrogen storage can effectively improve the flexibility of system scheduling. Then, taking several scenarios as simulation examples, the hierarchical energy supply strategy can effectively reduce the system operation cost and carbon emission cost, and it also has a certain role in realizing the local consumption of renewable energy.

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“…For example, in Ref. [14], an energy allocation technique was proposed to encourage customers to adjust their energy demand by choosing different types of energy to increase the utilization of wind power. The potential of an IES comprising multiple energy sources to provide demand-side management has been illustrated in Ref.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of on‐Grid Integrated Energy System Considering Peak‐Shaving Demand Via Learning Methods

Tang

et al. 2022

IEEJ Transactions Elec Engng

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An integrated energy system (IES) is combined with electricity, gas and heating subnetworks to satisfy various loads, which results in a complicated energy coupling. Considering the power interaction between IES and power grid, the real‐time dynamic optimal dispatch policies for the on‐grid IES are proposed and compared by learning methods to improve the operation economic and peak‐shaving performances in this study. Owing to the uncertainties of the load demands and real‐time peak‐shaving demand and renewable energy, together with the sequential operation of the controllable sources, the energy dispatch optimization problem of the IES is described as a stochastic dynamic optimization process. A source‐load collaborative operation mode is proposed as the coupling of thermal energy and electrical power in the IES and compared with different energy dispatching mode. And reinforcement learning methods are adopted to achieve the dynamic optimal policy. Since the multiple systems with energy coupling would result in modeling difficulty and dimension curse during the optimization, a deep reinforcement learning method (DDQN) is used to solve this problem. A numerical analysis is performed by comprehensively comparing the different learning algorithms and operation modes, and the simulation results show that the derived dispatch policy achieved by DDQN method with source‐load collaboration can effectively improve the performance of the IES in the stochastic environment. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Demand response from multiple-energy customers in integrated energy system

Cited by 13 publications

References 18 publications

Optimal configuration and sizing of regional energy service company's energy hub with integrated demand response

Optimal configuration and sizing of regional energy service company's energy hub with integrated demand response

Research on the optimal scheduling of a multi- storage combined integrated energy system based on an energy supply grading strategy

Dynamic Optimization of on‐Grid Integrated Energy System Considering Peak‐Shaving Demand Via Learning Methods

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