Review on combined wind power generation and energy storage systems

As one of the key paths for promoting green and low-carbon development, distributed wind and photovoltaic power generation and energy storage systems (referred to as wind-solar-storage systems) are indispensable for the new power system by leveraging their cleanliness, renewability, and flexible dispatch capabilities. This review elucidates integration mechanism, key technologies, and application value in green development of distributed wind-solar-storage energy systems. It systematically reviews and analyses the current research progress and application practices in system architecture, optimization dispatch, intelligent control, and energy storage management both domestically and internationally. Then, it points out the main challenges currently faced include high system integration complexity, high energy storage costs, and difficulties in control coordination. It also proposes future development directions from multiple dimensions, such as adaptive dispatch mechanisms based on artificial intelligence, novel and efficient energy storage technologies, and coordinated optimization of source-grid-load-storage. This paper can provide theoretical references and methodological insights for the integration and optimization research of distributed renewable energy systems.

Large scale exploitation of Renewable Energy Sources (RES) and their sustainable integration in the power system are important and challenging goals for transmission system operators because of the unpredictability of most of these sources. The aim of this paper is to highlight the advantages, in terms of enhanced RES exploitation and improved system flexibility, deriving from the coordinated management of wind power generation and energy storage systems: in particular, a Residual Unit Commitment procedure is proposed in order to provide the generation resources required to satisfy reserve and transmission network constraints. The resulting model consists in a large scale mixed-integer programming problem which is solved by the CPLEX optimization package. Tests on the IEEE 24 bus system will be presented.

Offshore oil and gas platforms can accelerate their shift towards lower greenhouse gases emissions by combining wind power generation and energy storage systems. However, the variability of the wind resources and power demand, and the limited storage capacity make the design of the system particularly challenging. We present a modelica library at its early stage of the development and yet with promising results. The model will be used to simulate the dynamic of the whole system for a long period of time using simplified power components and balanced micro grid. This paper shares some preliminary results by applying the model on a data set of wind resources and power demand from the North Sea. The results are the power system dynamics and an approximation of the mass of greenhouse gas emissions. On the long term the model will be applied to determine the right control and optimization strategy to control the energy system towards lower greenhouse gas emissions without compromising the balance between the power supply and demand.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

With the fast development of clean energy in China, the conflict between limited peak load regulation capability and the increasing installed wind power capacity became more and more obvious. Two different wind power integration dispatching modes were proposed in this paper, meanwhile, the wind power integration capacity and system economy under different scenarios, which have different wind power penetration and energy storage system (ESS) capacity, were also analyzed. As indicated in the example analysis, the ESS can significantly enhance the wind power integration capacity; and the economical wind power integration dispatching mode is the most suitable choice for the power grid in wind-rich area.

Currently, virtual power plants (VPP) in urban areas face problems such as over-dispersion of distributed power sources, insufficient source-load coordination. Traditional VPP planning method increases the difficulty in the operation and the dispatch of VPP. Therefore, this paper proposes a planning method for urban VPP that fully considers the coupling characteristics between the buses. From the distribution network to district to bus, a multi-objective bilevel optimization planning model for urban VPP is established. The upper-level planning model aims at minimizing the annual comprehensive cost, and derive the overall capacity of wind power turbine (WT), photovoltaic(PV), and energy storage system (ESS) of VPP in each district. The lower-level planning model aims at minimizing the network loss, and realizes the site selection and capacity allocation in each selected site. The case study based on the IEEE-33 standard system was carried out to verify the advancement and effectiveness of the proposed method.

According to characteristics of the power grid in China, a framework of microgrid for distributed generations (DGs) integration is proposed. To ensure the reliability of power supply, the framework access distribution network by single-bus section. The DGs consist of photovoltaic (PV) system, wind power system and energy storage system. Based on the proposed framework and control strategy, the models of distributed generations were built in DIgSILENT/PowerFactory. According to the control strategy, three cases were analyzed: 1) the dynamic operating characteristics of the microgrid from grid-connected mode to isolated mode; 2) the dynamic operating characteristics of the microgrid with wind speed fluctuation disturbance in grid-connected mode and islanded mode; 3) the influence to microgrid operation in different fault types and locations. The simulation results show that the proposed framework of microgrid conforms to Chinas power grid feature and the control strategy can ensure safety and reliable operation under different situation.

Wind Power utilization has been growing at a rapid rate all over the world. India has been keeping pace with this great green development by retaining its 5th position in installed capacity of grid connected 19.9GW of Wind Power. The article addresses the history of growth of wind turbines in India, and the status of development driven by several factors including technical, economical, fiscal and financial apart from meeting the goals for National action plan for climate change. In the personal opinion of the author, identifies broad areas for the wayforward in India, such as enabling policy sustainability, investment grade wind potential assessment, evacuation infrastructure and smart grids for wind power, Manufacturing capacity for wind turbine generators, cost of energy reduction using consortium based approach, Infirmity management through focused Spinning reserve capacity generation, Research for excess wind power usage and energy storage systems, On-shore and off-shore wind power, and Small Wind/Solar systems – SWES (hybrid) for all. The discussions and details are pertaining to best practices in India.

Energy-efficient system and charge balancing topology for electric vehicle application

Different from conventional generators, wind power is an intermittent energy source, power system dispatch and control is confronted with new challenges. This paper establish the mathematical model of wind power and energy storage system for simulation and analysis, and study the wind power prediction using statistical methods and physical methods, and study the key technologies of automatic generation control and automatic voltage control which were in the large-scale wind power and energy storage system. The results show that the wind power prediction is reasonable for grid, and the power quality and stability of wind farm connected to power grid can be well improved by battery energy storage.

A series of challenges have been put forward for safe operation and stable control of the power grid with integration of renewable energy, such as wind, photovoltaic, etc., and also novel loads such as electric vehicles. It stimulates the technology development of smart grid. This paper introduces a demonstration project of an island smart grid in China. The renewable energy resources on this island are mainly wind, photovoltaic and biomass energy. The penetration level of the renewable energy will up to 40% in 2015 and the full load will be powered by the renewable energy in the future. And the whole island will build the power supply and service system for electric vehicles, the green farm energy supply system characterized by biomass make full use of, the generation system combined wind power and energy storage system, the micro grid characterized by building photovoltaic, and industrial micro grid with induction motor load and power energy storage.

This study examines the issue of wind power smoothing in renewable-energy-grid integration scenarios. Under the “dual-carbon” policy initiative, large-scale renewable energy integration (particularly wind power) has become a global focus. However, the intermittency and uncertainty of wind power output exacerbate grid power fluctuations, posing challenges to power system stability. Consequently, smoothing strategies for wind power energy storage systems are desperately needed to improve operational economics and grid stability. According to current research, single energy storage technologies are unable to satisfy both the system-level economic operating requirements and high-frequency power fluctuation compensation at the same time, resulting in a trade-off between economic efficiency and precision of frequency regulation. Therefore, hybrid energy storage technologies have emerged as a key research focus in wind power energy storage. This study employs the SE-SGMD method, utilizing the distinct characteristics of lithium batteries and supercapacitors to decompose frequency regulation commands into low- and high-frequency components via frequency separation strategies, thereby controlling the output of supercapacitors and lithium batteries, respectively. Additionally, the GA-GWO algorithm is applied to optimize energy-storage-system configuration, with experimental validation conducted. The theoretical contributions of this study include the following: (1) introducing the SE-SGMD frequency separation strategy into hybrid energy storage systems, overcoming the performance limitations of single energy storage devices, and (2) developing a power allocation mechanism on the basis of the inherent properties of energy storage devices. In terms of methodological innovation, the designed hybrid GA-GWO algorithm achieves a balance between optimization accuracy and efficiency. Compared to PSO-DE and GWO-PSO, the GA-GWO energy storage system demonstrates improvements of 21.10% and 17.47% in revenue, along with reductions of 6.26% and 12.57% in costs, respectively.

There is growing interest in Energy Storage Systems (ESS) and other distributed energy resources (DER) as non-wires alternatives to resolve distribution issues while also providing valuable services to the grid and to energy customers. One such use is to defer distribution capacity investments required due to load and/or DER growth. Selecting ESS power and energy ratings for capacity deferral requires time-series load profile data. This paper is a part of a longer-term objective of making it easy for utilities to consider energy storage systems within distribution planning. This paper proposes a practical planning-based approach to screen ESS (both power and energy ratings) to defer distribution capacity investments. More specifically, an approach is presented here that leverages linear power flow approximation to quickly perform many time-series ESS dispatch simulations enabling rapid ESS project screening for a large number of distribution feeders. This paper also illustrates intuitive ways to visualize optimal ESS power and energy ratings for different peak clipping objectives. The proposed ESS project screening approach is demonstrated on a real Hydro One distribution feeder.

In view of the frequency fluctuation of the micro-grid system containing wind power, hybrid energy storage system composed of batteries and supercapacitors is adopted to coordinate the output, so as to maintain the frequency stability of the micro-grid and provide the possibility for the micro-grid to further absorb wind power and other clean energy. Firstly, by analyzing the characteristics of wind power output, the power deviation signal of the system is decomposed into low-frequency and high-frequency parts through wavelet packet decomposition, so as to maximize the consumption of wind power output power on the premise of meeting the stability of the system. Then, the high frequency part of the power gap is further divided to determine the initial FM power distribution scheme of the energy storage system. Finally, the energy storage output is modified by the Logistic regression function to take into account both the energy storage adaptive frequency modulation and the energy storage charged state self-recovery. Example analysis shows that the proposed control strategy can realize the optimal distribution of wind power, ensure the full utilization of wind farm output, improve wind power grid connection capacity, and increase efficiency and economy.

Integrating residential photovoltaic (PV) power generation and energy storage systems into the Smart Grid is an effective way of reducing fossil fuel consumptions. This has become a particularly interesting problem with the introduction of dynamic electricity energy pricing, since consumers can use their PV-based energy generation and controllable energy storage devices for peak shaving on their power demand profile, thereby minimizing their electricity bill. A realistic electricity pricing function is considered with billing period of a month, comprising both an energy price component and a demand price component. Due to the characteristics of electricity price function and energy storage capacity limitation, the residential storage control algorithm should 1)utilize PV power generation and load power consumption predictions and 2)account for various energy loss components during system operation, including energy loss components due to rate capacity effect in the storage system and power dissipation of the power conversion circuitry. A near-optimal storage control algorithm is proposed accounting for these aspects. The near-optimal algorithm, which controls the charging/discharging of the storage system, is effectively implemented by solving a convex optimization problem at the beginning of each day with polynomial time complexity. For further improvement, the reinforcement learning technique is adopted to adaptively determine the residual energy in the storage system at the end of each day in a billing period.