Integrated scheduling of renewable generation and demand response programs in a microgrid

Mazidi, Mohammadreza; Zakariazadeh, Alireza; Jadid, Shahram; Siano, Pierluigi

doi:10.1016/j.enconman.2014.06.078

Cited by 356 publications

(167 citation statements)

References 51 publications

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“…As reported in [5][6][7], it is very difficult to predict wind output because of the high randomness of the wind speed, which makes it troublesome to accurately control the small-scaled wind turbine system. Moreover, scattered solar radiation increases the difficulty in predicting photovoltaic (PV) panel output, which is clearly demonstrated in [8][9][10]. Therefore, after adding a small-scaled portable wind turbine and a set of PV panels to the distributed microgrid, system uncertainty can be increased to some extent.…”

Section: Introductionmentioning

confidence: 99%

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

Zhang

Gong

et al. 2018

Applied Sciences

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Abstract:With the rapid growth of electricity demands, many traditional distributed networks cannot cover their peak demands, especially in the evening. Additionally, with the interconnection of distributed electrical and thermal grids, system operational flexibility and energy efficiency can be affected as well. Therefore, by adding a portable energy system and a heat storage tank to the traditional distributed system, this paper proposes a newly defined distributed network to deal with the aforementioned problems. Simulation results show that by adding a portable energy system, fossil fuel energy consumption and daily operation cost can be reduced by 8% and 28.29%, respectively. Moreover, system peak load regulating capacity can be significantly improved. However, by introducing the portable energy system to the grid, system uncertainty can be increased to some extent. Therefore, chance constrained programming is proposed to control the system while considering system uncertainty. By applying Particle Swarm Optimization-Monte Carlo to solve the chance constrained programming, results show that power system economy and uncertainty can be compromised by selecting appropriate confidence levels α and β. It is also reported that by installing an extra heat storage tank, combined heat and power energy efficiency can be significantly improved and the installation capacity of the battery can be reduced.

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Section: Introductionmentioning

confidence: 99%

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

Zhang

Gong

et al. 2018

Applied Sciences

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“…This triad is also valuable for organising research efforts shedding light into these challenges. Technological adaptation-oriented research has contributed with knowledge on the integration of electricity storage in distribution systems [37][38][39][40][41], integration of distributed generation sources from wind [42][43][44], solar [45][46][47][48][49], CHP [50][51][52], and micro-CHP [53][54][55][56], integration of EVs [57][58][59][60], integration of smart meters [61][62][63][64][65][66], implementation of DR [67][68][69][70][71], deployment of active distribution management systems [72][73][74][75][76], and advanced grid monitoring and control [77][78][79], as well as the use of artificial intelligence methods [80][81][82], and machine learning applicati...…”

Section: Electricity Distribution Adaptation To a Smarter And More Sumentioning

confidence: 99%

The challenging paradigm of interrelated energy systems towards a more sustainable future

Soares

Martins

Carvalho³

et al. 2018

Renewable and Sustainable Energy Reviews

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This paper brings together several contemporary topics in energy systems aiming to provide a literature review based reflection on how several interrelated energy systems can contribute together to a more sustainable world. Some directions are discussed, such as the improvement of the energy efficiency and environmental performance of the systems, the development of new technologies, the increase of the use of renewable energy sources, the promotion of holistic and multidisciplinary studies, and the implementation of new management rules and "eco-friendly and sustainable" oriented policies at different scales. The interrelations of the diverse energy systems are also discussed in order to address their main social-economic-environmental impacts. The subjects covered include the assessment of the electricity market and its main players (demand, supply, distribution), the evaluation of some urban systems (buildings, transportation, commuting), the analysis of the implementation of renewable energy cooperatives, the discussion of the diffusion of the electric vehicle and the importance of new bioenergy systems. This paper also presents relevant research carried out in the framework of both the Energy for Sustainability Initiative of the University of Coimbra and the Sustainable Energy Systems focus area of the MIT-Portugal Program. To conclude, several research topics that should be addressed in the near future are proposed.

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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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Integrated scheduling of renewable generation and demand response programs in a microgrid

Cited by 356 publications

References 51 publications

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

The challenging paradigm of interrelated energy systems towards a more sustainable future

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

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