A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs

Tavakoli, Mehdi; Shokridehaki, Fatemeh; Marzband, Mousa; Godina, Radu; Pouresmaeil, Edris

doi:10.1016/j.scs.2018.05.035

Cited by 110 publications

(63 citation statements)

References 29 publications

(42 reference statements)

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“…The convergence to small group solutions will be avoided; (vi) a new offspring is generated and the values of objective functions are evaluated; (vii) ranks assignment and niche count calculation are carried out repeatedly in the new offspring; and (viii) the algorithm is terminated if it attains the maximum number of generations or if the output reaches the pareto front. It is noted that there exist other stopping criteria in literature for stochastic optimization algorithm and can be referred to [29][30][31]. carried out repeatedly in the new offspring; and (viii) the algorithm is terminated if it attains the maximum number of generations or if the output reaches the pareto front.…”

Section: Optimal Design Of Ga-svm-mkl Classifiermentioning

confidence: 99%

Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption

2018

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Energy sustainability is one of the key questions that drive the debate on cities’ and urban areas development. In parallel, artificial intelligence and cognitive computing have emerged as catalysts in the process aimed at designing and optimizing smart services’ supply and utilization in urban space. The latter are paramount in the domain of energy provision and consumption. This paper offers an insight into pilot systems and prototypes that showcase in which ways artificial intelligence can offer critical support in the process of attaining energy sustainability in smart cities. To this end, this paper examines smart metering and non-intrusive load monitoring (NILM) to make a case for the latter’s value added in context of profiling electric appliances’ electricity consumption. By employing the findings in context of smart cities research, the paper then adds to the debate on energy sustainability in urban space. Existing research tends to be limited by data granularity (not in high frequency) and consideration of about six kinds of appliances. In this paper, a hybrid genetic algorithm support vector machine multiple kernel learning approach (GA-SVM-MKL) is proposed for NILM, with consideration of 20 kinds of appliance. Genetic algorithm helps to solve the multi-objective optimization problem and design the optimal kernel function based on various kernel properties. The performance indicators are sensitivity (Se), specificity (Sp) and overall accuracy (OA) of the classifier. First, the performance evaluation of proposed GA-SVM-MKL achieves Se of 92.1%, Sp of 91.5% and OA of 91.8%. Second, the percentage improvement of performance indicators using proposed method is more than 21% compared with traditional kernel. Third, results reveal that by keeping different modes of electric appliance as identical class label, the performance indicators can increase to about 15%. Forth, tunable modes of GA-SVM-MKL classifier are proposed to further enhance the performance indicators up to 7%. Overall, this paper is a bold and novel contribution to the debate on energy utilization and sustainability in urban spaces as it integrates insights from artificial intelligence, IoT, and big data analytics and queries them in a context defined by energy sustainability in smart cities.

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Section: Optimal Design Of Ga-svm-mkl Classifiermentioning

confidence: 99%

Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption

2018

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“…The charge and discharge power of ES should not exceed its capacity limit, which can be described by Equations (9)- (11). Meanwhile, in the vast majority of case, ES cannot be charged and discharged at the same time t simultaneously, as expressed in Equation (12).…”

Section: Model Of Energy Storagementioning

confidence: 99%

“…Currently, electricity demand response has been widely used in power systems for peak-load shifting, to obtain the benefits of reducing the reserve capacity and postponing equipment investment [10,11]. With more and more implementation of distributed generation and microgrid, DR starts to be actively applied in commercial building microgrids and residential energy systems [12,13]. In [14], a framework for home microgrids considering coalition game and demand-side management is presented.…”

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confidence: 99%

Bilevel Optimal Dispatch Strategy for a Multi-Energy System of Industrial Parks by Considering Integrated Demand Response

Zhao

Peng

et al. 2018

Energies

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To combat energy shortage, the multi-energy system has gained increasing interest in contemporary society. In order to fully utilize adjustable multi-energy resources on the demand side and reduce interactive compensation, this paper presents an integrated demand response (IDR) model in consideration of conventional load-shedding and novel resource-shifting, due to the fact that participants in IDR can use more abundant resources to reduce the consumption of energy. In the proposed IDR, cooling, heating, electricity, gas and so forth are considered, which takes the connection between compensation and load reductions into consideration. Furthermore, a bilevel optimal dispatch strategy is proposed to decrease the difficulty in coordinated control and interaction between lower-level factories and upper-level multi-energy operators in industrial parks. In this strategy, resources in both multi-energy operator and user sides are optimally controlled and scheduled to maximize the benefits under peak shifting constraint. In the normal operation mode, this strategy can maximize the benefits to users and multi-energy operators. Particularly in heavy load conditions, compared to the conventional electricity demand response, there are more types of adjustable resources, more flexibility, and lower interactive compensations in IDR. The results indicate that optimal operation for factories and multi-energy operators can be achieved under peak shifting constraint and the overall peak power value in industrial park is reduced.

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“…A novel framework is presented in [22] to study the impacts of investment incentives on generation expansion planning. Based on a two-stage energy management strategy, wind power and plug-in electrical vehicles were able to be used to minimize its operational cost and maintain the power balance in [23]. In reference [24], with the hierarchical framework, the battery energy-storage system and renewable energy sources could be combined to realize an economic generation schedule.…”

Section: Introductionmentioning

confidence: 99%

Research on Modeling and Hierarchical Scheduling of a Generalized Multi-Source Energy Storage System in an Integrated Energy Distribution System

Wang

Liu

et al. 2019

Energies

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Energy storage systems play a crucial role in ensuring stable operation. However, the development of system-level energy storage is hindered due to the restrictions of economy, geography, and other factors. Transitions of traditional power systems into integrated energy distribution systems (IEDS) have provided new solutions to the problems mentioned above. Through intelligent control management methods, the utilization of multi-energy-type resources both on the supply and demand sides shows the potential for equivalent storage characteristics. Inspired by the aggregation principles, this paper aims at proposing a novel model named generalized multi-source energy storage (GMSES), including the modeling and cooperation of three kinds of available resources: conventional energy storage (CES), multi-energy flow resources (MFR), and demand response resources (DRR). Compared with the conventional means of storage, GMSES can be regarded as a more cost-effective and flexible participant in the proposed hierarchical energy scheduling framework that can realize system-level storage services in IEDS. On this basis, a multi-timescale energy scheduling strategy is proposed to reshape the regulation of IEDS operations and deal with the fluctuations caused by renewable energy and loads, where the general parameter serialization (GPS)-based control strategy is utilized to select and control the responsive loads in DRR. Furthermore, a hierarchical scheduling algorithm is developed to generate the optimal set-points of GMSES. Case studies are analyzed in an electricity-gas coupled IEDS. The simulation results show that the coupled co-optimization GMSES model is conducive to achieving the goal of self-management and economical operation, while the influence of the underlying IEDS on the upper energy system is reduced, as the tie-line power fluctuations are smoothed out.

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A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs

Cited by 110 publications

References 29 publications

Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption

Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption

Bilevel Optimal Dispatch Strategy for a Multi-Energy System of Industrial Parks by Considering Integrated Demand Response

Research on Modeling and Hierarchical Scheduling of a Generalized Multi-Source Energy Storage System in an Integrated Energy Distribution System

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