Rising environmental concerns are integrating more renewables in power systems. This increase introduces uncertainty in generation and makes it challenging to maintain a balance between demand and supply. To avoid balancing problems and consequent stability issues, better forecast models are needed as traditional techniques are not fully equipped to deal with these new challenges. Thus, artificial intelligence (AI) based forecast techniques are gaining potential recognition in the realm of electricity markets. This paper aims at investigating the state-of-art of AI applications for price forecasts in electricity balancing markets (EBMs). The focus of previous studies extended in this direction has been towards the dayahead markets, whereas studies targeting EBMs are rather scarce. This paper shows how AI-based forecasts support EBMs modeling, resulting in more secure grid integration of distributed technologies. The benefits driven from such forecasts by market participants like brokers and customers are also investigated.
Battery energy storage systems (BESSs) have gained potential recognition for the grid services they can offer to power systems. Choosing an appropriate BESS location plays a key role in maximizing benefits from those services. This paper aims at analyzing the significance of site selection for placement of BESS in a power grid by providing a techno-economic evaluation with respect to specific grid services it can deliver, and benefits that can be extracted from those services in the form of revenue streams. The focus of the previous studies extended in this direction has been limited to the optimization techniques and software tools being used for BESS siting. However, questions around the benefits that stakeholders can derive from BESSs located at different levels of power network still remain unanswered. This paper handles those questions by drawing a link between technical considerations essential for BESS placement and their economic evaluations.
Battery energy storage systems (BESSs) are gaining potential recognition in modern power systems. They enable higher renewable shares in power networks by overcoming issues introduced by the intermittent nature of renewable resources. BESSs also provide various grid services such as frequency regulation, voltage support, energy management, and black start. Choosing an appropriate BESS location plays a key role in maximizing benefits from its services. This paper aims at investigating BESS placement for providing grid services at the point of installation. The previous studies extended in this direction have not considered the requirements of a real project under which BESS is being deployed and have mainly proposed solutions for standard IEEE bus systems. Also, the focus has not been on providing ancillary services using BESS, but mainly on loss minimization. This paper, on the other hand, presents a case study on the BESS placement problem by investigating various potential locations in Bornholm Island for fulfilling the objectives of a BESS-related industrial project, namely BOSS. This is achieved by considering factors like stackability of BESS-services, integration of large-scale renewable resources, and viability of business models.
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