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• W3193722263 endingPage "121744" @default.
• W3193722263 startingPage "121744" @default.
• W3193722263 abstract "Deployment of photovoltaic (PV) systems in nearly zero energy buildings is rapidly increasing, negatively affecting grid stability and power quality. Therefore, many utilities favour policies that limit the power injection to the grid and increase PV self-consumption. A battery energy storage system (BESS) could constitute a possible solution. However, BESS optimal scheduling to increase PV self-consumption and minimize potential purchased energy from the grid in the situation of non-supported demand by PV is a non-linear multi-variable decision-making problem. Previous works have used optimization algorithms to overcome this problem. Due to these algorithms' nature, instead of global optimum scheduling, just near-optimal results are achieved and the consistency of the results, which is essential especially for sensitivity analysis is not guaranteed. The main goal of this paper is to present an analytical approach to maximize the PV self-consumption and optimize BESS scheduling to minimize electricity costs for a two-level electricity tariff energy system. The effectiveness of the proposed analytical scheduling of the BESS-PV energy system is demonstrated through simulation on real data of a Nearly Zero Energy Building (nZEB) in the Baltic region, Estonia, as the case study. Compared with alternative optimization algorithms, the proposed approach leads to a significant reduction in the purchased energy from the grid, while maximizing PV self-consumption and also noticeably reducing computation complexity and time for reaching the global solution." @default.
• W3193722263 created "2021-08-30" @default.
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• W3193722263 date "2022-01-01" @default.
• W3193722263 modified "2023-10-01" @default.
• W3193722263 title "Analytical approach for maximizing self-consumption of nearly zero energy buildings- case study: Baltic region" @default.
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• W3193722263 doi "https://doi.org/10.1016/j.energy.2021.121744" @default.
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