Optimization of residential battery energy storage system scheduling for cost and emissions reductions

Olivieri, Zachary T.; McConky, Katie

doi:10.1016/j.enbuild.2020.109787

Cited by 33 publications

(22 citation statements)

References 29 publications

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“…However, in our opinion, selling energy is not always the best solution (or not allowed by energy provider), as it could be, in some cases, better to save this energy for future use when purchased energy price will increase. Olivieri and McConky (2020) present an innovative optimization model used to develop optimal battery charge and discharge schedules under three different objectives: minimize time dependent energy costs, minimize carbon emissions, and a multi-objective model that considers both energy costs and carbon emissions by including a social cost of carbon. In the same vein, Haidar et al (2018) propose a real-time consumer-dependent energy management system for smart buildings, which is designed to find a trade-off between the energy cost (either renewable or non-renewable) and its carbon impact.…”

Section: Renewablementioning

confidence: 99%

Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs

Ortiz

Kubler

Rondeau

et al. 2021

Journal of Cleaner Production

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Section: Renewablementioning

confidence: 99%

Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs

Ortiz

Kubler

Rondeau

et al. 2021

Journal of Cleaner Production

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“…The maximum injected power by the LIB, which is passed through the converter and inverter, is 41.95 kW, which is 31% less than that in the previous scenario. The cost of LIBESS can be calculated using Equation (12): It can be seen that the LIBESS cost shows a 49.7% and a 66.1% reductions in comparison to the first and second scenarios, respectively. As the thermal energy consumption is the same, the variations of indoor temperatures of those residences with air-conditioning TCLs are the same as those in the previous scenarios, which is shown in Figure 11.…”

Section: Fems Of Islanded Mg With 11 Thermostatically Controlled Tementioning

confidence: 99%

Energy management of islanded microgrid by coordinated application of thermal and electrical energy storage systems

Bagheri-Sanjareh

Nazari

Hosseinian

2020

Intl J of Energy Research

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The installation of an energy storage system (ESS) is vital for the Micrgorid (MG) islanded operation to ensure the maintenance of demand-supply balance. Lithium-ion batteries (LIBs) are among the most commonly used ESS technologies for grid-based applications, which is used in this paper to constantly maintain the demand-supply balance in a residential MG. For this purpose, a novel frequency-based energy management scheme is proposed. It uses an LIB ESS to handle the primary frequency control and energy management during peak-load period, while the dispatchable distributed generators like microturbine and fuel cell supply the base load. The airconditioning TCLs consume a significant part of the residential load profile, especially during midsummer. Unlike TCLs that instantaneously use their generated cooling thermal energy to control indoor temperatures, the thermal energy storage systems (TESSs) can also store the thermal energy and use it later. In this paper, the TESSs are used instead of TCLs to reduce the power consumption of a residential Microgrid (MG) in islanded mode. By doing this, the required LIBESSs capacity is decreased 70% considerably than the case the TCLs are used for controlling the indoor temperatures. Also, replacing the ACs and EHPs with TESSs results in a 63.7% reduction in the total cost of electrical and thermal storage.

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“…The model is based on a MILP formulation in which V2G and demand response strategies are considered. As some recent works in this area, we can reefer to [16][17][18].…”

Section: Introductionmentioning

confidence: 97%

Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization Approach

et al. 2021

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Generally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction.

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Optimization of residential battery energy storage system scheduling for cost and emissions reductions

Cited by 33 publications

References 29 publications

Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs

Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs

Energy management of islanded microgrid by coordinated application of thermal and electrical energy storage systems

Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization Approach

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