Energy management system for smart grid: An overview and key issues

As a result of the increased awareness of the dangers posed by global climate changes (mainly caused by growing global energy consumption needs), the quest for clean and sustainable energy future is becoming of paramount importance. This can be largely realized via a large-scale integration of variable renewable energy sources (RESs) such as wind and solar, which have relatively low carbon footprints. In many power systems, the level of integration of such resources is dramatically increasing. However, their intermittent nature poses significant challenges in the predominantly conventional power systems that currently exist. Among others, frequency and voltage regulation issues can, for example, arise because of improperly balanced and largely uncoordinated RES supply and demand. Generally, the higher the integration level of intermittent power sources is, the higher the flexibility needs are in the system under consideration. Flexibility, in a power systems context, refers to the ability of such a system to effectively cope with unforeseen changes in operational situations, which are mainly induced by the inherent uncertainty and variability arising from the supply side, demand side or any other external factors. In the absence of appropriate flexibility mechanisms, it is increasingly difficult to manage the imbalances between generation and demand as a result of their natural variations in real-time. This paper presents an extensive and critical review of the main existing and emerging flexibility options that can be deployed in power systems to support the integration of “carbon-free” and variable power production technologies. Starting from a broader definition of flexibility, we highlight the growing importance of such flexibility in renewable-rich energy systems, and provide insights into the challenges and opportunities associated with various flexibility options provided by different technologies.

A comprehensive survey of flexibility options for supporting the low-carbon energy future

Thermal energy capacity of buildings can be coupled to power networks via heating, ventilating, and air-conditioning (HVAC) units. Optimizing the operation of HVAC systems in multizone buildings is a challenging task, as occupants have different thermal preferences dependent on time-varying indoor and outdoor environments. To estimate the social cost of demand response (DR), building aggregators need to assess occupants’ thermal discomfort (TD), which is related to their productivity outcomes. This paper proposes a price-based DR strategy for multizone office buildings to co-optimize the energy cost of HVAC units and the TD levels of occupants. To overcome simplified TD representations, a mobile interface is developed that grants occupants the ability to indicate their personal thermal preferences. These preferences are modeled using artificial neural networks, which are explicitly and directly integrated into the optimal DR scheduling. In addition, we evaluate the thermal response of a multizone office building to the operation of a variable speed heat pump (VSHP). Using the models of occupants’ TD and building thermal response, the optimization problem for the proposed DR strategy is formulated and solved with mixed-integer linear programming. The case study results verify that the proposed strategy successfully optimizes VSHP operations and occupants’ TD levels, mitigating the risk of occupant interruption to the optimal DR schedule.

Optimal Price Based Demand Response of HVAC Systems in Multizone Office Buildings Considering Thermal Preferences of Individual Occupants Buildings

The smart power systems are based upon information and communication technologies, which lead to a deluge of data originating from various sources. To address these challenges concerning accumulated voluminous data, big data analysis in smart power systems is inevitable. This article comprehensively surveys the literature related to the big data issues in smart power systems. The background and motivation of the big data paradigm in smart power systems are first provided, and then the major issues related to the architectures, the key technologies, and standardizations of big data analytics in smart power systems are analyzed. Also, the potential applications of big data in smart power systems based upon the state-of-the-art research are highlighted. Finally, the future issues and challenges of the big data issues in modern power systems are discussed.

Big Data Issues in Smart Grids: A Survey

This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

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Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

We describe the background and an analytical framework for a mathematical optimization model for home energy management systems (HEMS) to manage electricity demand on the smart grid by efficiently shifting electricity loads of households from peak times to off-peak times. We illustrate the flexibility of the model by modularizing various available technologies such as plug-in electric vehicles, battery storage, and automatic windows. First, the analysis shows that the end-user can accrue economic benefits by shifting consumer loads away from higher-priced periods. Specifically, we assessed the most likely sources of value to be derived from demand response technologies. Therefore, wide adoption of such modeling could create significant cost savings for consumers. Second, the findings are promising for the further development of more intelligent HEMS in the residential sector. Third, we formulated a smart grid valuation framework that is helpful for interpreting the model's results concerning the efficiency of current smart appliances and their respective prices. Finally, we explain the model's benefits, the major concerns when the model is applied in the real world, and the possible future areas that can be explored.

Ryan Skorupski

Papers

A Mathematical Programming Formulation for Optimal Load Shifting of Electricity Demand for the Smart Grid