Energy management in microgrids with renewable energy sources: A literature review

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed

Model predictive control for microgrid functionalities: review and future challenges

ABSTRACT: Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.info:eu-repo/semantics/publishedVersio

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

A review of networked microgrid protection: Architectures, challenges, solutions, and future trends

The design and selection of advanced protection schemes have become essential for the reliable and secure operation of networked microgrids. Various protection schemes that allow the correct operation of microgrids have been proposed for individual systems in different topologies and connections. Nevertheless, the protection schemes for networked microgrids are still in development, and further research is required to design and operate advanced protection in interconnected systems. The interconnection of these microgrids in different nodes with various interconnection technologies increases the fault occurrence and complicates the protection operation. This paper aims to point out the challenges in developing protection for networked microgrids, potential solutions, and research areas that need to be addressed for their development. First, this article presents a systematic analysis of the different microgrid clusters proposed since 2016, including several architectures of networked microgrids, operation modes, components, and utilization of renewable sources, which have not been widely explored in previous review papers. Second, the paper presents a discussion on the protection systems currently available for microgrid clusters, current challenges, and solutions that have been proposed for these systems. Finally, it discusses the trend of protection schemes in networked microgrids and presents some conclusions related to implementation

An Online Hierarchical Energy Management System for Energy Communities, Complying with the Current Technical Legislation Framework

Efforts in the fight against Climate Change are increasingly oriented towards new energy efficiency strategies in Smart Grids (SGs). In 2018, with proper legislation, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy, aiming at reducing bill costs by taking advantage of proper incentives. That action aspires to accelerate the spread of local renewable energy exploitation, whose costs could not be within everyone's reach. Since a REC is technically an SG, the strategies above can be applied, and specifically, practical Energy Management Systems (EMSs) are required. Therefore, in this work, an online Hierarchical EMS (HEMS) is synthesized for REC cost minimization to evaluate its superiority over a local self-consumption approach. EU technical indications (as inherited from Italy) are diligently followed, aiming for results that are as realistic as possible. Power flows between REC nodes, or Microgrids (MGs) are optimized by taking Energy Storage Systems (ESSs) and PV plant costs, energy purchase costs, and REC incentives. A hybrid Fuzzy Inference System - Genetic Algorithm (FIS-GA) model is implemented with the GA encoding the FIS parameters. Power generation and consumption, which are the overall system input, are predicted by a LSTM trained on historical data. The proposed hierarchical model achieves good precision in short computation times and outperforms the self-consumption approach, leading to about 20% savings compared to the latter. In addition, the Explainable AI (XAI), which characterizes the model through the FIS, makes results more reliable thanks to an excellent human interpretation level. To finish, the HEMS is parametrized so that it is straightforward to switch to another Country's technical legislation framework.Comment: 26 pages, 18 figure

A review of hierarchical control for building microgrids

Building microgrids have emerged as an advantageous alternative for tackling environmental issues while enhancing the electricity distribution system. However, uncertainties in power generation, electricity prices and power consumption, along with stringent requirements concerning power quality restrain the wider development of building microgrids. This is due to the complexity of designing a reliable and robust energy management system. Within this context, hierarchical control has proved suitable for handling different requirements simultaneously so that it can satisfactorily adapt to building environments. In this paper, a comprehensive literature review of the main hierarchical control algorithms for building microgrids is discussed and compared, emphasising their most important strengths and weaknesses. Accordingly, a detailed explanation of the primary, secondary and tertiary levels is presented, highlighting the role of each control layer in adapting building microgrids to current and future electrical grid structures. Finally, some insights for forthcoming building prosumers are outlined, identifying certain barriers when dealing with building microgrid communities

Voltage control strategies for loss minimzation in autonomous microgrids

This dissertation investigates the novel idea of flexible-voltage autonomous microgrids (MG), employing several interconnectable dc buses operating in a minimum-voltage mode. In comparison with the traditional fixed-voltage MGs, the proposed MGs reduce losses to gain significant enhancement in efficiency. It is widely believed that energy systems of the future will heavily depend on MGs rich in power electronics converters (PECs). This dissertation is focused on MGs with a high degree of self-sufficiency, without precluding sporadic links with the power grid. Potential applications of those MGs include: (a) distributed generation power systems, (b) ships, land vehicles, aircraft, and spacecraft, (c) users in need of power supply impervious to vulnerabilities of the grid, and (d) localities lacking an access to a grid.Modern pulse-width modulated PECs allow rapid and wide-range changes of voltages and currents. High switching frequencies result in high power quality and fast dynamic response, but each switching event causes energy loss related to the magnitudes of input voltage and output current. In the existing MGs, the bus voltages are maintained at a fixed level. However, many heavy loads, such as electric drives, operate most of the time with a reduced voltage, which is adjusted by decreasing the voltage gain of the feeding converter. This makes the voltage pulses high and narrow. If instead the pulses were made wide and low, then with the current unchanged the conduction losses would remain unchanged, but the switching losses would greatly decrease. This observation leads to the main idea of the dissertation, namely MGs whose dc-bus voltages are allowed to fluctuate and which are maintained at the lowest possible level. Loss minimization, apart from energy savings, may be critical for autonomous MGs with a tight balance of power.In this dissertation, two methods are proposed for calculating the minimum (optimum) required dc voltage level. In the first method, a central control unit allocates the minimum required dc voltages to individual buses by employing the information obtained from control systems of the adjustable voltage loads. For example, most of the variable-speed ac motors employ the so-called constant volts per hertz strategy, in which the relation between frequency and voltage is clearly specified. In the more sophisticated high-performance drives, the instantaneous values of the desired speed, torque, and current are available, allowing the required voltage estimation from the equation of power balance.In the second method, the problem of determining the optimal dc voltage and power settings is formulated as an optimization problem with the objective function of minimizing the converter losses. Genetic algorithm is utilized in solving the optimization problem. Due to limited available power from renewables, reducing the converter losses will enhance the survivability of the microgrid and ease the cooling requirements, resulting in a more compact system. A model of a 20-bus microgrid with the dc distribution network is employed to verify the effectiveness of the proposed methods