Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response

Liu, Wenxia; Huang, Yu-Chen; Li, Zhengzhou; Yang, Yong; Fang, Yi

doi:10.1016/j.energy.2020.117279

Cited by 61 publications

(16 citation statements)

References 31 publications

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“…Genetic algorithm √ Multiple optimal solutions can be obtained and provided by integrating simulation models into optimization tools [8] Genetic algorithm and a nonlinear interior point method × Hybrid optimization algorithm is more effective for specific model [13] Particle swarm optimization √ The convergence speed is fast [9] Artificial neural network × It has fault tolerance and can solve nonlinear problems [10] Sequential quadratic programming algorithm × More effective in solving nonlinear constrained optimization problems [16] The optimal IES usually operates under uncertain conditions, through system design [17]. Liu et al [18] introduced subjective and cognitive uncertainties into the integrated demand response (IDR) model based on price-and introduced evidence theory and credibility levels to handle the double uncertainties. The final results showed that the risk of system operation could be reduced by considering the uncertainty of IDR, but the cost increased slightly.…”

Section: Algorithm Name Multi-objective Advantages Sourcementioning

confidence: 99%

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Multi-Objective Optimization and Performance Assessments of an Integrated Energy System Based on Fuel, Wind and Solar Energies

Zhao

2021

Entropy

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The integrated energy system (IES) is an efficient method for improving the utilization of renewable energy. This paper proposes an IES based on fuel, wind and solar energies, following an optimization study focused on determining optimal device capacities. The study included gas turbines, wind turbines, solar photovoltaic panels, ground source heat pumps, absorption chillers/heaters, batteries, and thermal storage. Objectives were incorporated into the optimization model for the overall performance of the IES; these included the primary energy saving rate, annual cost-saving rate, and carbon dioxide emission reduction. Then, the nondominated sorting genetic algorithm II was employed to solve the optimization problem for multiple objectives. Ultimately, the verification and sensitivity analyses of the optimization method were achieved by a case study of hospital buildings in Harbin. The optimization results indicated a primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of 17.3%, 39.8%, and 53.8%, respectively. The total installed capacity for renewable energy generation accounted for 64.5% of performance optimization. Moreover, the price of natural gas affected the economic indicators of the IES–but failed to impact energy consumption indicators.

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Section: Algorithm Name Multi-objective Advantages Sourcementioning

confidence: 99%

“…Tan et al [7] established various load combination prediction models based on multitask learning and least-squares support vector machines. The results showed that the average prediction accuracy of the model was improved by 18.60% in comparison with extreme learning.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization and Performance Assessments of an Integrated Energy System Based on Fuel, Wind and Solar Energies

Zhao

2021

Entropy

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“…Advanced physical information technology and innovative management models are utilized in IES, which integrates multiple energy sources such as coal, oil, natural gas, electrical energy, and thermal energy in the area. Thus, different energy can support each other to enhance the sustainability, flexibility and reliability of the system 31,32 …”

Section: Integrated Energy Systemmentioning

confidence: 99%

An attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system

Wu¹,

Wu²,

Liang³

et al. 2020

Int Trans Electr Energ Syst

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“…M. J. Sanjari et al [4] discussed the integration of energy storage equipment into the operating system of an integrated energy system and analyzed the problems caused by load behavior on the system operation scheduling. Liu et al [5] used evidence theory to describe the uncertainty of demand response and established the demand response model of an integrated energy system. Su et al [6] established an optimization model of an integrated reliable energy system based on the characteristics of each unit of the integrated energy system and considering the uncertainty of renewable energy, demands, and operations.…”

Section: Introductionmentioning

confidence: 99%

Optimal Planning Design of a District-Level Integrated Energy System Considering the Impacts of Multi-Dimensional Uncertainties: A Multi-Objective Interval Optimization Method

et al. 2021

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Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-objective interval optimization framework for the energy hub (EH) planning problem from the perspective of the source load synergy, while considering the impacts of both supply-and demand-side uncertainties. For this aim, based on an in-depth analysis of the adjustable characteristics of various loads in EH and their effect on RES absorption, an interval model is first established to describe the responsiveness of users' load demand to real-time energy price variations and its associated uncertainties. In view of the natural contradiction between the system's economic and environmental benefits, a multi-objective interval optimization model for the EH planning problem is developed, wherein the minimization of the system's economic costs and the maximization of the RES utilization rate are considered as the dual objectives to be optimized simultaneously. Moreover, this study takes into account the uncertainties of RES availability and demand-side behaviors by using interval numbers and properly considering their impacts in the context of long-term planning. According to the features of the proposed model, the interval order relation and possible degree method are jointly used to transform the model into a deterministic optimization problem first, and then an improved non-dominant sorting genetic algorithm is used to derive the optimal solution to the problem. The results show that the proposed method can effectively improve the economy of EH and the utilization efficiency of RES and flexibly meet different planning requirements, giving better engineering practicability.INDEX TERMS demand-side management, improved non-dominated sorting genetic algorithm, multiobjective interval optimization model, source load synergy, uncertainty analysis

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Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response

Cited by 61 publications

References 31 publications

Multi-Objective Optimization and Performance Assessments of an Integrated Energy System Based on Fuel, Wind and Solar Energies

Multi-Objective Optimization and Performance Assessments of an Integrated Energy System Based on Fuel, Wind and Solar Energies

An attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system

Optimal Planning Design of a District-Level Integrated Energy System Considering the Impacts of Multi-Dimensional Uncertainties: A Multi-Objective Interval Optimization Method

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