Microgrids are now emerging from lab benches and pilot demonstration sites into commercial markets, driven by technological improvements, falling costs, a proven track record, and growing recognition of their benefits. They are being used to improve reliability and resilience of electrical grids, to manage the addition of distributed clean energy resources like wind and solar photovoltaic (PV) generation to reduce fossil fuel emissions, and to provide electricity in areas not served by centralized electrical infrastructure. This review article (1) explains what a microgrid is, and (2) provides a multi-disciplinary portrait of today's microgrid drivers, real-world applications, challenges, and future prospects.

/pdf/microgrids-a-review-of-technologies-key-drivers-and-3wo8bifusj.pdf

Microgrids: A review of technologies, key drivers, and outstanding issues

The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.

State of the Art in Research on Microgrids: A Review

Microgrid is a new concept for future energy distribution system that enables renewable energy integration. It generally consists of multiple distributed generators that are usually interfaced to the grid through power inverters. For the islanding operation of ac microgrids, two important tasks are to share the load demand among multiple parallel connected inverters proportionately, and maintain the voltage and frequency stabilities. This paper reviews and categorizes various approaches of power sharing control principles. Simultaneously, the control schemes are graphically illustrated. Moreover, various control approaches are compared in terms of their respective advantages and disadvantages. Finally, this paper presents the future trends.

/pdf/review-of-power-sharing-control-strategies-for-islanding-q596scyml6.pdf

Review of Power Sharing Control Strategies for Islanding Operation of AC Microgrids

Microgrids energy management systems: A critical review on methods, solutions, and prospects

The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control.

Distributed Control Techniques in Microgrids

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

Trends in Microgrid Control

This paper presents the mathematical formulation of the microgrid's energy management problem and its implementation in a centralized Energy Management System (EMS) for isolated microgrids Using the model predictive control technique, the optimal operation of the microgrid is determined using an extended horizon of evaluation and recourse, which allows a proper dispatch of the energy storage units The energy management problem is decomposed into Unit Commitment (UC) and Optimal Power Flow (OPF) problems in order to avoid a mixed-integer non-linear formulation The microgrid is modeled as a three-phase unbalanced system with presence of both dispatchable and non-dispatchable distributed generation The proposed EMS is tested in an isolated microgrid based on a CIGRE medium-voltage benchmark system Results justify the need for detailed three-phase models of the microgrid in order to properly account for voltage limits and procure reactive power support

A Centralized Energy Management System for Isolated Microgrids

The issue of controlled and reliable integration of distributed energy resources into microgrids and large power grids has recently gained considerable attention. The microgrid concept, which basically corresponds to the coordinated operation of a cluster of loads, distributed generators and energy storage systems, is quite appealing due to its flexibility, controllability and energy management capabilities. In order to provide uninterruptible power supply to the loads, microgrids are expected to operate in both grid-connected and stand-alone modes, and economically meet the demand on an instantaneous basis. The problem of optimal management of the resources in a microgrid is being widely investigated and recent studies have proposed the application of both centralized and distributed control schemes by using multi-agent systems, heuristic methods and optimization algorithms. This paper elaborates on the conceptual design of a centralized energy management system (EMS) and its desirable attributes for a microgrid in stand-alone mode of operation. A number of test protocols are proposed to analyze the performance of the system, as well as the impacts of relevant parameters.

A centralized optimal energy management system for microgrids

An energy management system (EMS) determines the dispatching of generation units based on an optimizer that requires the forecasting of both renewable resources and loads. The forecasting system discussed in this paper includes a representation of the uncertainties associated with renewable resources and loads. The proposed modeling generates fuzzy prediction interval models that incorporate an uncertainty representation of future predictions. The model is demonstrated using solar and wind generation and local load data from a real microgrid in Huatacondo, Chile, for one-day ahead forecasts to obtain the expected values together with fuzzy prediction intervals to represent future measurement bounds with a certain coverage probability. The proposed prediction interval models would help to enable the development of robust microgrid EMS.

/pdf/fuzzy-prediction-interval-models-for-forecasting-renewable-188thtywvs.pdf

Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids

This paper presents the mathematical formulation and control architecture of a stochastic-predictive energy management system for isolated microgrids. The proposed strategy addresses uncertainty using a two-stage decision process combined with a receding horizon approach. The first stage decision variables (unit commitment) are determined using a stochastic mixed-integer linear programming formulation, whereas the second stage variables (optimal power flow) are refined using a nonlinear programming formulation. This novel approach was tested on a modified CIGRE test system under different configurations comparing the results with respect to a deterministic approach. The results show the appropriateness of the method to account for uncertainty in the power forecast.

Daniel E. Olivares

Papers

Trends in Microgrid Control

A Centralized Energy Management System for Isolated Microgrids

A centralized optimal energy management system for microgrids

Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids

Stochastic-Predictive Energy Management System for Isolated Microgrids