A new perspective for sizing of distributed generation and energy storage for smart households under demand response

Erdinç, Ozan; Paterakis, Nikolaos G.; Pappi, Iliana N.; Bakirtzis, Anastasios G.; Catalào, João P. S.

doi:10.1016/j.apenergy.2015.01.025

Cited by 146 publications

(91 citation statements)

References 34 publications

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“…The cost of generation should be considered in the users' daily cost. In this paper, the cost of PV generation is calculated by dividing the total cost of PV, including those for investment, operation, and maintenance, which is expressed as the cost of PV generation per unit electricity [18]. Hence, the daily cost of PV generation can be expressed as follows:…”

Section: Pv Generationmentioning

confidence: 99%

“…The problem of optimizing storage capacity in DSM programs has also been studied in some literatures with implementing different methods. The authors [18] propose a mixed-integer linear programming framework for DSM to optimize the capacity of PV and an energy storage system when considering the variability of loads during weekday-weekend and different seasons. Guo et al [19] provided an online control algorithm, which incorporated renewable energy generation, home appliances and battery storage, to achieve a clear trade-off between users' cost and energy storage capacity.…”

Section: Introductionmentioning

confidence: 99%

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Game-theoretic energy management with storage capacity optimization in the smart grids

Gao

Liu

Wei

et al. 2018

J. Mod. Power Syst. Clean Energy

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With the development of smart grids, a renewable energy generation system has been introduced into a smart house. The generation system usually supplies a storage system with the capability to store the produced energy for satisfying a user's future demand. In this paper, the main objective is to determine the best strategies of energy consumption and optimal storage capacities for residential users, which are both closely related to the energy cost of the users. Energy management with storage capacity optimization is studied by considering the cost of renewable energy generation, depreciation cost of storage and bidirectional energy trading. To minimize the cost to residential users, the non-cooperative game-theoretic method is employed to formulate the model that combines energy consumption and storage capacity optimization. The distributed algorithm is presented to understand the Nash equilibrium which can guarantee Pareto optimality in terms of minimizing the energy cost. Simulation results show that the proposed game approach can significantly benefit residential users. Furthermore, it also contributes to reducing the peak-to-average ratio (PAR) of overall energy demand.

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Section: Pv Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Game-theoretic energy management with storage capacity optimization in the smart grids

Gao

Liu

Wei

et al. 2018

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…2.3. Though the impact of the SOC limits on such capability has been extensively incorporated in [36,125,126,129,133], the impact of the SOC-dependent charging/discharging current [124] and voltage has not been thoroughly investigated in the literature.…”

Section: ) Modelling Ev Pv and Besmentioning

confidence: 99%

“…However, many other papers in the literature have not taken any of these impacts into account; rather these papers have intended to minimize either the power exchange or the cost involved. References [29,61,99,131,135] [ 49,56,120,126,129,133] For such optimal sizing exercise, a charging strategy is required to be employed to reduce these impact simultaneously (i.e., to minimize power exchange) and costs involved while maximizing the QoS of charging defined earlier. As such, though the proposed charging strategy in [56] maximizes the revenue generated, it is not clear how it is going to ensure the QoS.…”

Section: ) Time Series Energy Balance Analysis (Tseba)mentioning

confidence: 99%

“…Deploy BES [124,[127][128][129] Reduce losses [36,128] Deploy both [30,36,[125][126][127] Reduce unbalance [36,119] Reduce loading [36] Reduce costs [36,86,118,123,126] Reduce GHG emissions [86,117] Reduce transformer, feeder capacities required [36,129] Improve voltages [36,75,116,127] Improve QoS [36] Even though these papers form a comprehensive realm of studies regarding whether to augment grid or deploy PV and BES, the following limitations can be pointed out,  The papers above have assumed that either augmenting/reconfiguring the grid/phase or deploying onsite PV and BES is the best solution for reducing the impact, costs, GHG and increasing QoS. However, in practice, their various combinations (e.g., grid augmentation and PV; grid augmentation, PV, and BES; etc.)…”

Section: 34mentioning

confidence: 99%

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PV-based EV charging station with vehicle-to-grid services for business premises

Islam¹

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The transportation sector is the second major contributor to the global greenhouse gas (GHG) emissions, next to electricity generation sector. GHG emissions are projected to grow in the future due to the over-reliance on fossil fuels for both electricity generation and transportation sectors.However, the growth can be stalled by gradually introducing electric vehicles (EVs) as they produce less GHG compared to their conventional counterparts. Despite that, their proliferation has been stunted by a completely new set of challenges in addition to a very high capital cost. These challenges include low driving ranges, scarcity of electric vehicle charging stations (EVCS), long charging time and inability to provide charging service readily. Nevertheless, some of these challenges can be addressed by installing EVCS at business premises (i.e., offices, universities, etc.) and augmenting EVCS with onsite PV and battery energy storage (BES). In that case, the impacts of EV charging on the grid will be less significant; charging services will be available readily, and GHG emissions growth can be reduced significantly. Moreover, EVCS, under suitable conditions, would be able to provide vehicle-to-grid (V2G) services, which makes EVCS more attractive.Despite such prospective opportunities, offhand planning and operational planning of EVCS would deem them unachievable. In addition, the inherent intermittency of PV, and EV and grids loads will pose considerable hindrance on availing those benefits.Hence, this research focusses on an EVCS with onsite PV and BES, in the perspective of planning and operational planning including V2G services. The cornerstone of the planning and operational planning is an appropriate EV load model that captures the uncertainties of EV charging. In addition, the EV load model reflects the grid voltage and EV battery's state of charge (SOC)-dependency of EV charging. Moreover, it accounts for the diversity of the EV population embodied by market shares, battery sizes, charging levels, and charging voltages, currents, and power factors. Furthermore, it is scalable to facilitate seamless assimilation of a large EV population. Therefore, a new EV load model is first proposed with MATLAB/Simulink validation reflecting the factors above along with the recommendations by the standard BS EN 61851-23:2014. This EV model is then incorporated into the planning activities, which include four chronological exercises. Firstly, the impact (i.e., grid voltage, current, losses, etc.) of the EV charging on the grid at different locations, namely home, office, public charging, is investigated considering the uncertainties. Secondly, the optimal location of the PV and BES based EVCS is divulged regarding the reduction of the impact and costs involved while enhancing the charging quality of service (QoS). Thirdly, a suitability analysis is performed for the obtained optimal location to find the most suitable combination of the grid upgrading, PV deployment, and BES iii deployment to satisfy the QoS, ...

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Multi‐objective unit and load commitment in smart homes considering uncertainties

Alilou

Tousi

Shayeghi

2020

Int Trans Electr Energ Syst

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Summary Although smart homes play an important role in implementing demand‐side management programs, the concept is currently faced with challenges in hourly scheduling of devices, because it is difficult for consumers to manually control appliances based on time‐varying prices and the power produced by local energy sources. Thus, the operating schedule of home appliances, renewable energy sources, and electric vehicles is optimized using a home energy management system in this paper. A price‐based demand response program is utilized for operational scheduling. Renewable energy resources and electric vehicle uncertainties as well as the hourly variation of solar irradiance and wind power are also taken into account. To achieve the best plan of devices operation, a technical‐economic formulation is developed for HEM problem, and the multi‐objective dragonfly algorithm is used for its optimization. The decision‐aid approach is adopted for choosing the best compromise solution, that is, selecting the best operational schedule of home appliances from the Pareto front. Numerical simulations illustrated the proper performance of the proposed method in finding the best schedule of home devices and improving the operation indices of the smart home.

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A new perspective for sizing of distributed generation and energy storage for smart households under demand response

Cited by 146 publications

References 34 publications

Game-theoretic energy management with storage capacity optimization in the smart grids

Game-theoretic energy management with storage capacity optimization in the smart grids

PV-based EV charging station with vehicle-to-grid services for business premises

Multi‐objective unit and load commitment in smart homes considering uncertainties

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