Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast
The paper [10] presents a novel adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)-based hybrid maximum power point tracking (MPPT) method for efficient photovoltaic power generation. The proposed method eliminates oscillations and achieves rapid and maximal power tracking without the need for additional
This way, the PV systems are able to provide flexible and reliable services even when the peak demand for electricity misalign with the window of most efficient PV power generation. In this study, we develop an integrated charge/discharge scheme for lithium-ion batteries to maximize their total expected benefit. Specifically, we develop a
In this work, a novel HEMS is proposed for the optimization of an electric battery operation in a real, online and data-driven environment that integrates state-of-the-art load forecasting...
Capacity scheduling (CS) is a crucial component of PV-BSS energy management, aiming to ensure the secure and economic operation of the PV-BSS. This article
There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to absorb extra power during the light load periods, BESS can also supply additional power under high load conditions. However, their capacity may not be
In this work, a novel HEMS is proposed for the optimization of an electric battery operation in a real, online and data-driven environment that integrates state-of-the-art load forecasting combining CNN and LSTM neural networks to increase the robustness of decisions.
In this work, a novel HEMS is proposed for the optimization of an electric battery operation in a real, online and data-driven environment that integrates state-of-the-art load
Capacity scheduling (CS) is a crucial component of PV-BSS energy management, aiming to ensure the secure and economic operation of the PV-BSS. This article proposes a Proximal Policy Optimization (PPO)-based deep reinforcement learning (DRL) agent to perform the CS of PV-BSS.
a deep reinforcement learning (RL) strategy, centered around the soft actor-critic (SAC) algorithm, to schedule a community battery system in the presence of uncertainties, such as solar photovoltaic (PV) generation, local demand, and real-time energy prices. We position the community battery to play a versatile role,
Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty
There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to
Photovoltaic (PV) has been extensively applied in buildings, adding a battery to building attached photovoltaic (BAPV) system can compensate for the fluctuating and unpredictable features of PV power generation. It is a potential solution to align power generation with the building demand and achieve greater use of PV power. However, the BAPV with
a deep reinforcement learning (RL) strategy, centered around the soft actor-critic (SAC) algorithm, to schedule a community battery system in the presence of uncertainties, such as solar
This way, the PV systems are able to provide flexible and reliable services even when the peak demand for electricity misalign with the window of most efficient PV power
The main objective for net-zero energy buildings is to attain a high level of self-sufficiency (Kumar et al., 2024, Brown et al., 2024).Matching the battery''s capacity with the building''s energy needs is crucial for maximising the rate at which self-generated energy is used (Ahmed et al., 2022, Li et al., 2022) addition, current models that prioritise economic
Our paper evaluates the potential of reducing investment needs through decentralized photovoltaic electricity generation and battery energy storage systems. We use power-flow analyses on representative grid models to test rural, urban, and suburban grids'' resilience to higher electric vehicle penetration. We find significant societal benefits
Reverse power flow also introduces additional loading and power losses in the distribution transformers and the primary feeder sections [3], [4]. Mitigating these challenges will reduce grid reinforcement costs and operational costs [5].
This paper aims to bridge this gap by studying the use of an RL-based method for joint design and control of a real-world PV and battery system. The design problem is first formulated as a
1 Introduction. Among the most advanced forms of power generation technology, photovoltaic (PV) power generation is becoming the most effective and realistic way to solve environmental and energy problems [].Generally, the integration of PV in a power system increases its reliability as the burden on the synchronous generator as well as on the
The paper [10] presents a novel adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)-based hybrid maximum power point tracking (MPPT)
Based on the operation and maintenance data of a photovoltaic power station, in order to realize the accurate fault diagnosis of a photovoltaic power generation system, a data-driven photovoltaic power generation system fault diagnosis method based on deep reinforcement learning is proposed. It is verified and analyzed by simulation, and the following conclusions
At the same time, the continuous growth of power demand and the uncertainty in photovoltaic power generation have significantly increased the scale and complexity of modern power systems. This has led to a higher probability of severe system failures, such as voltage violations, unplanned islanding, and reverse power flow, significantly impacting the normal operation of
The paper [10] presents a novel adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)-based hybrid maximum power point tracking (MPPT) method for efficient photovoltaic power generation. The proposed method eliminates oscillations and achieves rapid and maximal power tracking without the need for additional sensors to measure
Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power
Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate...
Aiming at the coordinated control of charging and swapping loads in complex environments, this research proposes an optimization strategy for microgrids with new energy charging and swapping stations based on adaptive multi-agent reinforcement learning. First, a microgrid model including charging and swapping loads, photovoltaic power generation, and
This paper aims to bridge this gap by studying the use of an RL-based method for joint design and control of a real-world PV and battery system. The design problem is first formulated as a mixed-integer linear programming problem (MILP).
Solar photovoltaic (PV) power generation is the process of converting energy from the sun into electricity using solar panels. Solar panels, also called PV panels, are combined into arrays in a PV system. PV systems
To address the challenges posed by the intermittence and randomicity of photovoltaic (PV) power generation in the existing power system, a hybrid deep learning model for accurate PV power forecasting is addressed.
In conclusion, the paper emphasizes the significance of PSCs as a practical solution to meet the increasing energy demands. While PSCs possess notable advantages in terms of efficiency, flexibility, and material diversity, there remains a need to enhance their affordability and applicability to fully harness the potential of solar energy.
It indicates the percentage of sunlight energy that is successfully converted into electrical energy by the system. Higher system efficiency values indicate more effective energy conversion, resulting in better overall performance of the photovoltaic system. Fig. 7. System Efficiency. 5. Conclusion
Photovoltaic solar cells (PSCs) have emerged as a promising technology in this context. PSCs offer numerous advantages by directly converting sunlight into electricity, including high efficiency, flexibility, and the ability to utilize diverse materials and manufacturing techniques .
This study concludes by emphasizing that ongoing research and development endeavors will result in improvements in the affordability, versatility, and overall efficiency of solar cells.
By reducing the cost of manufacturing and installation, solar cells can become more economically competitive with traditional energy sources, encouraging widespread adoption . This affordability aspect plays a significant role in maximizing solar energy utilization.
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