Design and intelligent control of building integrated photovoltaic system

Hu, Qiqi; Tang, Ruoli

doi:10.1063/1.4996582

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…For Mediumterm management (from an hour to a day), the building control system is charged to control the dispatchable energy sources in coordination in order to decrease the imported energy from the grid utility, and enhance the operation of the global hybrid system. Many researchers have proposed various methods in this way [5][6][7][8][9][10][11][12]. However, most of the previous studies do not take into account the building power flow behaviour for the next time intervals, which is highly related to weather conditions regarding power consumption or renewable sources generation.…”

Section: Introductionmentioning

confidence: 99%

One‐day ahead predictive management of building hybrid power system improving energy cost and batteries lifetime

Habib

Ladjici

Bollin

et al. 2019

IET Renewable Power Generation

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In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.

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Section: Introductionmentioning

confidence: 99%

One‐day ahead predictive management of building hybrid power system improving energy cost and batteries lifetime

Habib

Ladjici

Bollin

et al. 2019

IET Renewable Power Generation

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show abstract

“…MPPT control of the large-scale BIPV system, Hu et al proposed a novel topological structure of the BIPV array, and the intelligent MPPT approach based on evolutionary algorithm is also developed. [17] However, it is well known that the degradation of PV module is greatly influenced by the climatic conditions. [18] Especially as the BIPV system operates under hot and humid climate, the PV module degradation may cause significant drift of PÀV and IÀV characteristics after years of operation.…”

mentioning

confidence: 99%

Dynamic Model and Intelligent Maximum Power Point Tracking Approach for Large‐Scale Building‐Integrated Photovoltaic System

Tang

2021

Energy Tech

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In the modern city, the concepts of "intelligent building," "green building," and "net-zero energy building" have attracted the attention of worldwide scholars. [1][2][3] As one of the most popular green energy systems, the photovoltaic (PV) power generation system plays an increasingly important role in engineering applications for achieving promising performances on energy saving and emission reduction. [4][5][6][7] For the so-called buildingintegrated photovoltaic system (BIPV), especially in high buildings with large surface area, thousands of PV panels are installed on the building facades for collecting solar energy. [8][9][10] As buildings account for almost 32% of the world's total electricity power consumption, the promotion and popularization of green building techniques (e.g., the BIPV system) are of great importance for the worldwide energy saving and emission reduction. [11] The maximum power point tracking (MPPT) technique is an important and necessary part in PV systems, as it can ensure the system keeps outputting the maximum power under varying environmental conditions. [12][13][14] As shown in Figure 1, the BIPV system always suffers from not only the complex shadings caused by its surroundings, but also the dynamic incidence sunlight angles caused by the unpredictable occupants' behaviors. For example, in the modern BIPV system, the facades of building are completely covered by a special PV module, namely, the BIPV window, which can capture the solar energy by integrating special PV materials and can also work as the traditional windows. As illustrated in Figure 1, each of the BIPV window can be closed or keep a random opening angle. This may lead to a dynamic incidence sunlight angle of each BIPV window and may also lead to random shading for its lower floor.As a result, the fast and accurate MPPT control of a large-scale BIPV system is always difficult. To overcome this problem, some efficient MPPT approaches are widely developed in recently years. For example, Liu et al. developed a hybrid MPPT approach by combining the variable step-size MPPT algorithm with continuous conduction mode and discontinuous current mode. Their proposed approach was applied in a BIPV system and verified by simulation experiments. [15] To overcome the partial shading problem of the facade BIPV system, Ravyts et al. developed a distributed MPPT by means of embedded converters and a common direct current bus. [16] According to the result of their case studies, the proposed approach can obtain promising performance with overall high efficiencies. For the design and

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Control and optimal management of grid-connected photovoltaic systems and micro-grids using artificial intelligence and metaheuristic techniques

Mellit¹,

Kalogirou²

2022

Handbook of Artificial Intelligence Techniques in Photovoltaic Systems

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Design and intelligent control of building integrated photovoltaic system

Cited by 8 publications

References 32 publications

One‐day ahead predictive management of building hybrid power system improving energy cost and batteries lifetime

One‐day ahead predictive management of building hybrid power system improving energy cost and batteries lifetime

Dynamic Model and Intelligent Maximum Power Point Tracking Approach for Large‐Scale Building‐Integrated Photovoltaic System

Control and optimal management of grid-connected photovoltaic systems and micro-grids using artificial intelligence and metaheuristic techniques

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