Coordinated Operation of Multi-Integrated Energy System Based on Linear Weighted Sum and Grasshopper Optimization Algorithm

Liu, Jinglu; Wang, Anna; Qu, Yanhua; Wang, Wenhui

doi:10.1109/access.2018.2859816

Cited by 60 publications

(38 citation statements)

References 36 publications

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“…(6) In the lower-level transaction, EUs are more sensitive to MEG's electricity price, and can adapt to the TOU price with dynamic load balancing. Taking the industrial EU as an example, in the valley price periods (1)(2)(3)(4)(5)(6)(7)(8)(19)(20)(21)(22)(23)(24), the industrial EU shifts a certain loads from peak periods to these periods, which results in the lower dissatisfaction. In the peak price periods (9-13, 14-18), the electrical demands are large, so the industrial EU cuts the non-essential loads as possible as it can, for reducing the expenditure.…”

Section: B Numerical Results Analysismentioning

confidence: 99%

“…The MEG responses to the TOU gas prices and reschedules its gas consumption to a certain degree. In the off-peak periods (1)(2)(3)(4)(5)(6)(7)(8)(19)(20)(21)(22)(23)(24), the gas consumption has a certain growth, except periods 21 and 22. This may be due to the lower gas prices in these periods.…”

Section: B Numerical Results Analysismentioning

confidence: 99%

“…The MEG is a multi-energy complementary system, where different types of energies are converted, stored and distributed in order to supply EUs with multiple energies, such as cooling, heating as well as electricity [2]. A variety of energy sources and the flexible structure make the MEG operation much more complicated [3]- [5]. Moreover, with the increasing number of MEGs, EUs and other market participants such as the natural gas company (NGC), participating in energy trading, the actions of them will influence the operation of the MEG.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Lin

Liu

2020

IEEE Access

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The application of time-of-use (TOU) pricing in the electricity market has significant value, which can improve economic efficiency and promote effective management of resources. However, this pricing mechanism has not been applied and promoted in the gas market in China. In this paper, we propose a bi-level transaction model considering TOU gas pricing for analyzing the economic efficiency of micro energy grid (MEG) operation based on Game Theory. The multiple energies trading is conducted between the natural gas company (NGC), the MEG and energy users (EUs), which forms a hierarchical Stackelberg game model. We first analyze utilities and strategies of aforementioned participants, and derive the Stackelberg equilibrium (SE) analytically as a balanced solution that captures the equilibrium strategies of participants. Then, a mixed integer nonlinear programming is formulated to determine the optimal TOU gas prices delivering maximum NGC profit, the optimal energy sales prices corresponding to the MEG' SE utility, and the optimal load pattern for EUs. Finally, numerical experiments are conducted in two scenarios, which reveal that the TOU pricing can well balance the gas supply and demand, and has significant potential for improving the economic efficiency of the MEG. INDEX TERMS Micro energy grid, Stackelberg game, economic efficiency, time-of-use pricing, equilibrium.

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Section: B Numerical Results Analysismentioning

confidence: 99%

Section: B Numerical Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Lin

Liu

2020

IEEE Access

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“…For one thing, IMESs are able to shift supply and demand across energy vectors and networks, as well as to handle the uncertain and volatile generation outputs of renewable energy sources (RES) [3], [4]. In addition, owing to the energy complementarity, the collaboration of MESs have the untapped potentials to jointly minimize the operational cost [5], enhance the overall operation efficiency and to increase system flexibility [2].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework

2019

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Motivated by the benefits of multi-energy integration, this paper establishes a bi-level two-stage framework based on transactive control, in order to achieve optimal energy provision among interconnected multi-energy systems (MESs). At the lower level, each MES autonomously determines the optimal set points of each controllable assets by solving a cost minimization problem, in which rolling horizon optimization is adopted to deal with load and renewable energies stochastic features. A technique is further implemented for optimization model convexification by relaxing storages complementarity constraints, and its mathematical proof verifies the exactness of the relaxation. At the upper level, a coordinator is established to minimize total costs of collaborative interconnected MESs while preventing transformer overloading. This collaborative problem is further decomposed and solved iteratively in a two-stage procedure based on market-clearing mechanism. A distinctive feature of the method is that it is compatible with operational time requirement, while retaining scalability, information privacy and operation authority of each MES. Effectiveness of the proposed framework is verified by simulation cases that conduct detailed analysis of the autonomous-collaborative optimization mechanism.

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“…Based on the modeling of detailed devices, an integrated energy system operation strategy is proposed and transformed into a mixed-integer linear programming model by linearization of nonlinear equations to solve the integrated energy system optimal scheduling problem [9]. To improve the overall energy efficiency and realize regional coordination optimization, an integrated strategy is proposed, which combines the linear weighted sum and the grasshopper optimization algorithms to solve the energy management problem in Reference [10]. The aforementioned papers have comprehensively considered system thermal and electrical loads when optimizing the operation of the integrated energy system, while they neglect the influence of cooling loads on system operation.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Operation Strategy Optimization for Integrated Energy Systems with Considering System Cooling Loads Independently

Yan

et al. 2018

Processes

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With the rapid growth of energy consumption, how to utilize energy in an efficient and cheap way becomes an intensive problem. This paper proposes an optimal operation strategy to reduce system fuel costs and increase system stability by independently considering cooling loads and adjusting CHP heat to power ratio seasonally. In this paper, a mathematical model of CHP operation is introduced to reveal the relationship between the supplementary volume of diesel oil and CHP heat to power ratio. Meanwhile, by analyzing the influence of seasonal factor on energy consumption, CHP heat to power ratio is optimized seasonally. Then, by independently considering the impacts of the cooling loads on system operation, the particle swarm optimization (PSO) algorithm is used to optimize the operation strategy of each device. Finally, this paper validates the positive effects of storage devices on improving system economy and stability under the premise of the time-of-use gas price. Results show that system fuel costs can be reduced by 5.2% if the seasonal factor is considered. Additionally, by optimizing the operation strategy, the peak valley gap of electrical loads in summer reduces by 40.7%. Moreover, the proposed strategy successfully utilizes storage capacity to shift loads and respond to gas price.

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Coordinated Operation of Multi-Integrated Energy System Based on Linear Weighted Sum and Grasshopper Optimization Algorithm

Cited by 60 publications

References 36 publications

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Economic Efficiency Analysis of Micro Energy Grid Considering Time-of-Use Gas Pricing

Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework

Seasonal Operation Strategy Optimization for Integrated Energy Systems with Considering System Cooling Loads Independently

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