Energy storage technology plays a role in improving new energy consumption capacities, ensuring the stable and economic operation of power systems, and promoting the widespread application of renewable energy technologies. Several new developments, ideas, approaches, and technologies have been introduced into this area from fields including materials, knowledge manage, electricity, control, and artificial intelligence. Based on the technical characteristics of renewable energy, this study reviews the roles, classifications, design optimisation methods, and applications of energy storage systems in power systems. First, we introduce the different types of energy storage technologies and applications, e.g. for utility-based power generation, transportation, heating, and cooling. Second, we briefly introduce the states of an energy storage system, along with its operation processes and energy storage capacity. Third, a comprehensive review is conducted on artificial intelligence applications in regards to optimisation system configuration, and energy control strategy, along with the applicability of different energy storage technologies. Finally, several issues and insights are discussed, offering new inspiration and concepts for the future study of integrated energy storage systems.

Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview

This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI’s role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption.

AI-Driven Urban Energy Solutions—From Individuals to Society: A Review

In this paper, a robust fractional-order PID (FOPID) controller is proposed to regulate islanded microgrid (MG) frequency. The considered MG is composed of a photovoltaic system, a wind turbine generation, a diesel generator, a battery energy storage system, the control unit, and loads. Some challenges in islanded MGs such as unpredictable variation in output power of renewable energy sources and model uncertainties, affect the system performance and lead to frequency deviations from the nominal value. For designing the proposed robust controller, the wind power and solar radiation are considered as disturbance inputs. Also, uncertainties are assumed in the inertia constant and the load damping coefficient parameters of the system. The FOPID parameters are determined by minimizing some constraints that guarantee robust stability and robust performance of the system. The performance of the proposed FOPID controller is compared with those of the classic PID and H∞ controllers. The effectiveness of the controller is illustrated through appropriate simulations.

Robust Control of Islanded Microgrid Frequency Using Fractional-Order PID

This paper presents the development of the improved hierarchical control system using the metaheuristic centralized PID tuner for DC microgrids. Hierarchical control is one of the best control strategies employed in photovoltaics (PV) based DC microgrids with three layers of primary, secondary, and tertiary controllers in which PID control is at the center of each one of these three layered control levels. The principal objective of the primary controller is to ensure near-equal power sharing among the units and of the secondary controller is to correct the deviations in the common DC link, while the tertiary controller is used to manage the energy flow among DC microgrids or between DC microgrid and the main utility grid. Partial shading, the uncertain nature of solar irradiation, and varying temperatures significantly reduce the overall power efficiency of traditionally tuned PID control-based hierarchical systems, since the tuning gains of these PID controllers are not adaptive to the dynamic processes. To optimize the control process, a novel hierarchical system is considered in which PID gains of primary, secondary, and tertiary controllers are tuned with metaheuristic moth-flame optimization to adapt to the variations. Matlab/Simulink simulations are performed to verify the efficiency of the proposed approach. The results highlight the superiority of the proposed method by utilizing process adaptive gains.

Development of an improved hierarchical control system using the metaheuristic pid tuner for dc microgrids

The energy storage system (ESS) is the next major disruption to the current architecture of the electricity grid. Energy storage offers several benefits to the electricity grid, which include frequency regulation, energy arbitrage, peak shaving, reducing intermittency of renewable energy sources (RESs), and many other benefits that are not possible without large energy storage. Large-scale batteries are still expensive and cannot be readily used with a reasonable return on investment. Aggregating a large number of consumers’ small scale batteries may provide us with the benefits of large central storage. However, aggregated small-scale batteries have several advantages over large central storage. Small-scale batteries provide better scalability and open up many new investment opportunities for the grid as well as for the consumers. In this article, we present a control scheme for small-scale distributed batteries, namely, Weighted Batteries Scheduling (WBS) scheme to make a large distributed energy storage. We also present a method to calculate weights, that are required for the WBS scheme, by prioritizing the batteries with respect to the state-of-charge (SOC). We evaluate the fairness of the proposed scheme using Jain’s fairness index and entropy-based fairness index. The proposed storage model can be used for any necessary support for the electricity grid. We study financial benefits obtained by the large distributed energy storage for frequency regulation, energy arbitrage and peak shaving. Frequency regulation appears to be of the highest value for energy storage. Our results show that a distributed storage consisting of 1000 small batteries each of 1 kW power achieves average daily revenue of $606.61.

/pdf/towards-developing-a-large-distributed-energy-storage-using-446sq964cq.pdf

Towards Developing a Large Distributed Energy Storage Using a Weighted Batteries Scheduling Scheme

The hierarchical control strategy of distributed energy storage units (DESUs) with mismatched line impedance and energy storage capacity is proposed for isolated DC microgrid. Firstly, the virtual impedance is dynamically adjusted by balance control to eliminate the influence of mismatched line impedance for the accuracy of current sharing. Then, each distributed energy storage unit can adjust current through SoC balance control according to its capacity and state of charge (SoC), make the error of SoC decrease, and gradually realize the SoC balance of DESUs during charging and discharging process. Finally, three simulation schemes based on Matlab are normal charging and discharging of DESUs, fluctuation of photovoltaic power and load change in DC microgrid. The results show that the proposed method can make current allocate reasonably according to capacity of DESUs.

Hierarchical Control Strategy for Distributed Energy Storage Units in isolated DC Microgrid

Frequency is the key indicator for island microgrid, but it can be affected by the renewable energy fluctuation and the load variation. The robust coordinated control strategy is proposed for the photovoltaic (PV) -diesel-battery isolated microgrid. Based on different frequency deviation ranges, PV systems and diesel generator (DG) systems are prioritized for frequency regulation. When the frequency deviation exceeds the set frequency threshold, the battery system begins to response quickly. Furthermore, the sliding mode (SM) load frequency control (LFC) is designed to improve the dynamic characteristics of the coordinated strategy and restrain the randomness and volatility of the source and load. The results of Matlab/Simulink prove that the proposed coordinated strategy can make full use of renewable energy and improve the frequency quality.

The Robust Coordinated Control Strategy for Isolated Microgrid

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Hierarchical Control Strategy for Distributed Energy Storage Units in isolated DC Microgrid

The Robust Coordinated Control Strategy for Isolated Microgrid