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

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Carbon capture and storage (CCS) is broadly recognised as having the potential to play a key role in meeting climate change targets, delivering low carbon heat and power, decarbonising industry and, more recently, its ability to facilitate the net removal of CO2 from the atmosphere. However, despite this broad consensus and its technical maturity, CCS has not yet been deployed on a scale commensurate with the ambitions articulated a decade ago. Thus, in this paper we review the current state-of-the-art of CO2 capture, transport, utilisation and storage from a multi-scale perspective, moving from the global to molecular scales. In light of the COP21 commitments to limit warming to less than 2 °C, we extend the remit of this study to include the key negative emissions technologies (NETs) of bioenergy with CCS (BECCS), and direct air capture (DAC). Cognisant of the non-technical barriers to deploying CCS, we reflect on recent experience from the UK's CCS commercialisation programme and consider the commercial and political barriers to the large-scale deployment of CCS. In all areas, we focus on identifying and clearly articulating the key research challenges that could usefully be addressed in the coming decade.

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Carbon capture and storage (CCS): the way forward

This paper provides an overview of grid code technical requirements regarding the connection of large wind farms to the electric power systems. The grid codes examined are generally compiled by transmission system operators (TSOs) of countries or regions with high wind penetration and therefore incorporate the accumulated experience after several years of system operation at significant wind penetration levels. The paper focuses on the most important technical requirements for wind farms, included in most grid codes, such as active and reactive power regulation, voltage and frequency operating limits and wind farm behaviour during grid disturbances. The paper also includes a review of modern wind turbine technologies, regarding their capability of satisfying the requirements set by the codes, demonstrating that recent developments in wind turbine technology provide wind farms with stability and regulation capabilities directly comparable to those of conventional generating plants.

A review of grid code technical requirements for wind farms

Plug-in electric vehicles (PEVs) present environmental and energy security advantages versus conventional gasoline vehicles. In the near future, the number of plug-in electric vehicles will likely grow significantly in the world. Despite the aforementioned advantages, the connection of PEV to the power grid poses a series of new challenges for electric utilities. This paper proposes a comprehensive approach for evaluating the impact of different levels of PEV penetration on distribution network investment and incremental energy losses. The proposed approach is based on the use of a large-scale distribution planning model which is used to analyze two real distribution areas. Obtained results show that depending on the charging strategies, investment costs can increase up to 15% of total actual distribution network investment costs, and energy losses can increase up to 40% in off-peak hours for a scenario with 60% of total vehicles being PEV.

Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks

The increasing number of PV system connected to LV distribution networks may trigger network reinforcement to maintain voltages and power flows within the statutory limits. The paper aims to investigate how different levels of Demand Response (DR) participation can facilitate the integration of PV. In order to get robust conclusion, fractal-based generic distribution network model is used to generate many statistically similar networks. Subsequently, a Particle Swarm Optimization approach to model the optimal demand pattern for various levels of DR participation is investigated in the study. The results suggest that DR can significantly improve self (local) consumption of PV output. As a result, network losses decrease and the voltage rise effect at high PV penetration can be alleviated.

Integration of PV system into LV distribution networks with Demand Response application

Proceedings of the 2011 IEEE Power and Energy Society General Meeting

This article investigates the use of a multi-state optimal power flow algorithm that simultaneously optimises the capacity utilisation of micro-generators to produce electricity in combination with...

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Maximising the utilisation of micro-generation using a multi-state optimal power flow

The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO2) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO2 emissions. Given the uncertainty around the future level of the corresponding CO2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance.

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A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain.

Danny Pudjianto

Papers

Integration of PV system into LV distribution networks with Demand Response application

Proceedings of the 2011 IEEE Power and Energy Society General Meeting

Maximising the utilisation of micro-generation using a multi-state optimal power flow

A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

Strategies for Continuous Balancing in Future Power Systems with High Wind and Solar Shares