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Multiple energy carrier systems stem from the need to evolve traditional electricity, gas and other energy systems to more efficient, integrated energy systems. An approach is presented, for controlling multiple energy carriers, including electricity (AC or DC), heat, natural gas and hydrogen, with the objective to minimise the overall cost and/or emissions, while adhering to technical and commercial constraints, such as network limits and market contracts. The technique of multi-agent systems (MAS) was used. The benefits of this approach are discussed and include a reduction of more than 50% in the balancing costs of a potential deviation. An implementation of this methodology is also presented. In order to validate the operation of the developed system, a number of experiments were performed using both software and hardware. The results validated the efficient operation of the developed system, proving its ability to optimise the operation of multiple energy carrier inputs within the context of an energy hub, using a hierarchical multi-agent system control structure. [ABSTRACT FROM AUTHOR]

Planned Communities (PCs) present a unique opportunity for deployment of intelligent control of demand-side distributed energy resources (DER) and storage, which may be organized in Microgrids (MGs). MGs require balancing for maintaining safe and resilient operation. This paper discusses the implications of using MG concepts for planning and control of energy systems within PCs. A novel tool is presented, based on decision trees (DTs), with two potential applications: (i) planning of energy storage systems within such MGs and (ii) controlling energy resources for energy balancing within a PC MG. The energy storage planning and energy balancing methodology is validated through sensitivity case studies, demonstrating its effectiveness. A test implementation is presented, utilizing distributed controller hardware to execute the energy balancing algorithm in real-time. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-agent system was designed for aggregated micro-generator emissions control. [•] Simulations of system operation were validated experimentally. [•] VPP emissions have been controlled with a good accuracy. [•] VPP controllability increased when the number of micro-generators was increased. [ABSTRACT FROM AUTHOR]

• Electric utility experience and challenges during the COVID-19 pandemic documented. • Practical conceptual framework is presented, for thinking about pandemic resilience. • CIGRE C4.47 definition of Power System Resilience applied to COVID-19 pandemic. • Case study using graph theory illustrates the risk of pandemics to utility assets. • Probabilistic epidemic spread study confirms effectiveness of mitigating measures. Resilience is a vital concept in engineering, business, and natural sciences, and is a measure of the ability of an entity to withstand High Impact Low Probability (HILP) events. During the COVID-19 pandemic, which started in late 2019/early 2020, power system utilities around the globe have responded in effective and efficient ways to enhance the resilience of their organisations, both in terms of real-time operations and prudent management of its infrastructure, in order to continue their mandate in providing reliable supply to meet customer demands. This paper presents the CIGRE definition for power system resilience, established by the C4.47 Working Group in 2018, and demonstrates the application of resilience-oriented thinking within the electrical sector. The response and recovery efforts are described, with respect to the key actionable measures integral to the power system resilience definition, taken before, during and after the COVID-19 pandemic. A practical conceptual framework is also presented for thinking about resilience in terms of three key components of resilience strategies: organisational, infrastructure and operational resilience. The paper also discusses the different strategies adopted in response to COVID-19, based on the C4.47 members' experiences during the pandemic. Finally, a case study is presented, which proves the effectiveness of a set of response measures, using graph theory and the characteristics of the staff-asset interactions. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-agent system was designed for the battery charging management of electric vehicles. [•] The real-time operation of the agent-based control system was demonstrated experimentally. [•] Electric vehicles were managed according to electricity prices and network constraints. [•] Electric vehicles provided active demand services. [ABSTRACT FROM AUTHOR]