Energy storage systems will play a key role in the power system of the twenty first century considering the large penetrations of variable renewable energy, growth in transport electrification and decentralisation of heating loads. Therefore reliable real time methods to optimise energy storage, demand response and generation are vital for power system operations. This paper presents a concise review of battery energy storage and an example of battery modelling for renewable energy applications and second details an adaptive approach to solve this load levelling problem with storage. A dynamic evolutionary model based on the first kind Volterra integral equation is used in both cases. A direct regularised numerical method is employed to find the least-cost dispatch of the battery in terms of integral equation solution. Validation on real data shows that the proposed evolutionary Volterra model effectively generalises conventional discrete integral model taking into account both SoH and the availability of generation/storage.

Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems are proposed using the advanced methods based on deep reinforcement learning and integral equations. First, the wind and solar irradiance potential of several sites on the lake Baikal’s banks is analyzed as well as the electric load as a function of the climatic conditions. The optimal selection of the energy storage system components is supported in online mode. The approach is justified using the retrospective meteorological datasets. Such a formulation will allow us to develop a number of valuable recommendations related to the optimal control of several autonomous AC/DC hybrid power systems with different structures, equipment composition and kind of AC or DC current. Developed approach provides the valuable information at different stages of AC/DC hybrid power systems projects development with stand-alone hybrid solar-wind power generation systems.

Recent blackouts in the USA, Europe and Russian Federation have clearly demonstrated that secure operation of large interconnected power systems cannot be achieved without full understanding of the system behavior during abnormal and emergency conditions. Current practice of managing separate parts of the system without knowledge of the `full picture' will lead to even greater blackouts. This paper proposes a novel approach to the system monitoring and control with the goal of identification of potential voltage instability problems before they lead to major blackouts. The proposed approach is based on detecting alarm states using self-organized Kohonen neural networks, and activating a multi-agent control system to take necessary preventive actions. The Kohonen network is trained off-line and then applied on-line to predict possible emergencies. The intelligent system was realized in STATISTICA 8.0 and tested on the modified 42-bus IEEE power system. Results are presented and discussed.