Multi-party energy management for smart building cluster with PV systems using automatic demand response

Ма, Ли; Liu, Nian; Wang, Lingfeng; Zhang, Jianhua; Lei, Jinyong; Zeng, Zheng; Wang, Cheng; Cheng, Mengchun

doi:10.1016/j.enbuild.2016.03.072

Cited by 114 publications

(41 citation statements)

References 23 publications

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“…As the electricity price has a great effect on the power consumption of end users [16], in this paper the DR is treated as an equivalent virtual generation unit in order to reflect the sensitivity of load consumption demand to the change electricity price. According to [17], the power consumption of end users with DR utilization can be expressed as:…”

Section: Demand Responsementioning

confidence: 99%

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Liu

Jie

Wang

2017

J. Mod. Power Syst. Clean Energy

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From the perspective of transactive energy, the energy trading among interconnected microgrids (MGs) is promising to improve the economy and reliability of system operations. In this paper, a distributed energy management method for interconnected operations of combined heat and power (CHP)-based MGs with demand response (DR) is proposed. First, the system model of operational cost including CHP, DR, renewable distributed sources, and diesel generation is introduced, where the DR is modeled as a virtual generation unit. Second, the optimal scheduling model is decentralized as several distributed scheduling models in accordance with the number of associated MGs. Moreover, a distributed iterative algorithm based on subgradient with dynamic search direction is proposed. During the iterative process, the information exchange between neighboring MGs is limited to Lagrange multipliers and expected purchasing energy. Finally, numerical results are given for an interconnected MGs system consisting of three MGs, and the effectiveness of the proposed method is verified.

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Section: Demand Responsementioning

confidence: 99%

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Liu

Jie

Wang

2017

J. Mod. Power Syst. Clean Energy

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“…Demand response technology can be used to optimize the operation of MG [11,12], frequency and connection line power [13,14], as well as support emergency fault [15,16]. DR can significantly improve the economy, reliability and flexibility of MG system [17].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

et al. 2016

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Abstract:In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR on BESS optimal sizing. Under the peak-valley time-of-use (TOU) price, this paper builds cross-time DR models based on price elasticity matrix. Furthermore, through the introduction of DR and BESS into PV-MG scheduling optimization, the MG investment and benefit model is proposed. Considering the constraint condition such as co-ordination of supply and demand, electricity price elasticity and energy loss of storage system, the improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimal allocation model of the BESS with the target of maximum PV consumptive rate and annual net profits. The optimization method was applied to a PV-MG in Guangdong. Through the regulation and control effect of demand response and BESS on load distribution, the uncertainties PV power can be suppressed so as to improve the PV system consumptive level, which is of great guiding significance for BESS optimal sizing under this situation.Keywords: time-of-use (TOU) price; multi-period demand response (DR); battery energy storage system (BESS); photovoltaic consumptive rate; investment and benefit model; multi-objective optimization; non-dominated sorting genetic algorithm II (NSGA-II) algorithm

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“…A residential energy hub is designed to coordinate solar energy with load demand and determine the scheduling of electric vehicles [9]. Particularly, there are multiple participants inside an EI, how to coordinate and optimize the interests of each participant is addressed in [10][11][12][13]. An optimization model of energy sharing among smart building is developed using non-cooperative game theory [10].…”

Section: Introductionmentioning

confidence: 99%

“…Until recently, the research of the EI has drawn wide attention, and its topics may include architecture system [5][6][7], coordination control [8,9] and energy management [10][11][12]. A future electric power distribution system was proposed in [5] to suit for the plug-and-play of DERs and distributed storage devices.…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach

et al. 2018

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Community Energy Internet (CEI) integrates electric network and thermal network based on combined heat and power (CHP) to improve the economy of energy system in Smart Community. In the CEI, an energy sharing framework for prosumers equipped with photovoltaic-thermal (PVT) system and heat pump (HP) is introduced. Supporting by the PVT and HP, the prosumer has four role attributes with either heat or electricity producer/consumer. A social welfare maximization model is built for the CEI, including PVT-HP prosumers, CHP system, and utility grid. Considering there are multiply participants in the local market of CEI, the social welfare maximization problem is decoupled by using Lagrange multiplier method. Moreover, a consensus-based fully distributed algorithm is designed to solve the problem. Finally, six residential buildings are selected as the case study to validate the effectiveness of the proposed method.

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Multi-party energy management for smart building cluster with PV systems using automatic demand response

Cited by 114 publications

References 23 publications

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach

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