Nations are facing today the transition to cleaner, more reliable and affordable energy systems where power grids are becoming less centralized, more flexible and digitalized, and where not only power utilities, but also consumers are playing a significant role. Distributed Energy Systems (DES) constitute a key element in such transition, with decentralized renewable energy generation near the consumption points, energy storage, electric vehicles, and energy management systems, with the potential to ensure continuous supply and achieve higher efficiency, while reducing costs and adverse environmental impacts. This chapter presents a review of the recent advances in the design and development of DES, focusing on the effect of taking into consideration the consumption profile and behaviour of the end-users. The chapter also revises the limitations of DES and summarizes the future directions of DES development.
<p>Demand-side flexibility is defined as the capacity to increase, decrease, or shift a fraction of the electricity consumption in a power system. This type of load management could increase the use of renewable energy sources and the reliability of distributed energy systems. Renewable energy sources, particularly wind and solar energy, are variable in time and usually, the periods of higher generation do not coincide with peak demand periods. This can reduce the share of such renewable energy sources in the energy balance of power systems. This work presents the modeling of a distributed energy system located in Colombia with solar photovoltaic and wind energy installations, as well as lithium-ion batteries. In the model, demand-side flexibility is applied to increase the share of renewable generation in the energy balance of the system, increase the system’s reliability and decrease the cost of electricity for consumers. The model is compared with a system that has no management of electricity consumption to assess the impact of demand-side flexibility on the share of renewables on the energy balance of the system and the system’s reliability.</p>
Power systems require the continuous balance of energy supply and demand for their appropriate functioning, which makes electricity forecast a necessary process for the successful planning of operation and expansion of modern power systems, especially with the increase of renewable energy resources to be accommodated in order to realize low-carbon power systems. The task of predicting electricity consumption is complex because electricity demand patterns are intricate and involve various factors such as weather conditions. Recurring Neural Networks (RNN), such as Long Short-Term Memory (LSTM) networks, can learn long sequence patterns and make multi-step forecasts at once considering several variables, which can be especially useful for time series forecasts such as electricity consumption. This paper presents the application and assessment of a multivariate multi-step times series forecasting model based on LSTM neural networks for short-term prediction of electricity consumption using a dataset that encompasses data on energy load and meteorological elements from Belgorod Oblast in Russia as a case study.
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