Optimal day-ahead scheduling of integrated urban energy systems

Jin, Xiaolong; Mu, Yunfei; Jia, Hongjie; Wu, Jianzhong; Xu, Xiandong; Yu, Xiaodan

doi:10.1016/j.apenergy.2016.07.071

Cited by 124 publications

(50 citation statements)

References 39 publications

(3 reference statements)

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“…Spatiotemporal predictions of energy use based on measures of human mobility are of paramount importance, since the continuing growth in both technology and urban populations [1] is anticipated to amplify the measure and variety of human activities, thus creating ever more complex built environments in our future cities, with even greater fluctuations in energy demands across time and space. Current urban scale energy predictive approaches [5][6][7]22,[58][59][60][61] generally overlook the effects of consumer behaviors and thus will eventually fall short in terms of reliability. This study represents the first attempt to use the human mobility of an urban population as an indicator of their daily activities for the spatiotemporal predictions of energy demand in city neighborhoods.…”

Section: Discussionmentioning

confidence: 99%

Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction

Taylor

2017

Applied Energy

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Urbanization is causing a significant increase in the amount, diversity, and complexity of human activities, all of which have a substantial impact on energy consumption. Current approaches to predicting energy demand at different spatiotemporal levels are functions of the characteristics of either individual buildings or cities and their occupancy levels, or are data-driven, typically taking the form of sensor-based modeling. Nevertheless, accounting for both the spatial and temporal effects of heterogeneous human behavior patterns on buildings' energy use at the city level remains a challenge. In this paper, we examine the temporal manifestation of the fluctuations of energy use in urban buildings driven by spatial mobility patterns of the population. We then present an urban-level spatiotemporal approach for predicting buildings' energy demand. Using a full year of individual positional records from an online social networking platform (Twitter), we introduce a multivariate autoregressive model in reduced principle component analysis space to create monthly predictions of residential building electricity demand generated across 801 spatial divisions that account for 68% of the electricity used by buildings in the City of Chicago, through a spatial autoregressive model. This model represents an important step forward, incorporating the spatiotemporal energy use fluctuations of urban population activities to create more reliable predictions of demand in future cities.

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Section: Discussionmentioning

confidence: 99%

Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction

Taylor

2017

Applied Energy

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“…Other optimization algorithms for solving feeder reconfiguration problem, e.g., hybrid heuristic algorithms [32]- [33], adaptive modified heuristic algorithm [34], mixed-integer convex programming [35], etc., are out of the scope of this paper, which would be studied in our future research work.…”

Section: ) Scheme Solving Algorithmmentioning

confidence: 99%

Alleviation of overloads in transmission network: A multi-level framework using the capability from active distribution network

Jin

Jia

et al. 2019

International Journal of Electrical Power & Energy Systems

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“…With respect to solving the optimal scheduling model, particle the swarm optimization method is used to calculate the output of Combined Cold, Heat and Power (CCHP) units [16]. A hybrid optimization method based on the genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the optimal day-ahead scheduling model for an integrated urban energy system [17]. To consider the uncertain energy input and output problem, reference [18] presents an improved particle swarm optimization and two-point estimate method for reducing the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

et al. 2019

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The optimal scheduling of multi-energy hub systems plays an important role in the safety, stability, and economic operation of the system. However, due to the strong uncertainty of renewable energy access, serious coupling, and the interaction among energy hubs of multi-energy hub systems, it is difficult for the traditional optimal scheduling method to solve these problems. Therefore, game theory was used to solve the optimal scheduling problem of multi-energy hub systems. According to the internal connection mode and energy conversion relationship of energy hubs, along with the competitive and cooperative relationship between multi-energy hubs, the game theoretic optimal scheduling model of the multi-energy hub system was established. Then, two cases and 50 groups of wind speed series were used to test the robustness of the proposed method. Simulation results show that the total power injection is −16,805.8, 104.1847, and −865.561 and the natural gas injection is 46,046.81, 27,727.65, and 63,039.54 in spring/autumn, summer, and winter, respectively, which is consistent with the characteristics of the four seasons. Furthermore, the optimal scheduling method using game theory has a strong robustness in multi-energy hub systems.

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Optimal day-ahead scheduling of integrated urban energy systems

Cited by 124 publications

References 39 publications

Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction

Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction

Alleviation of overloads in transmission network: A multi-level framework using the capability from active distribution network

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

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