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This article introduces emissions response, the widespread use of real-time emissions factors in electricity grids as a signal for a dynamic response to facilitate decarbonization and system efficiency. Many publications have suggested dynamic approaches to addressing systemwide emissions, reducing emissions through time-sensitive consumption and aiding in the adoption of renewable and low-emissions technologies. With emissions…
Degradation of energy capacity and energy efficiency of lithium-ion battery energy storage systems depend on their operation conditions and are key factors in their business case feasibility. Using traditional energy reservoir models with energy throughput methods to quantify degradation leads to less accurate revenue estimates and unfeasible dispatch scenarios. This paper proposes a new neural…
In this paper, a new deep learning-based two-stage dataset-clustering/temporal-clustering method is proposed for time aggregation in renewable energy-integrated power systems. In this way, for the first time, the representative period, including one or more representative days, is obtained using a GAN-based model in which the LSTM Network is embedded in both generator and discriminator models.…
The integration of wind energy into the power grid has been on the rise, becoming an increasingly important source of electricity. However, the intermittency of wind energy output, particularly the wind power ramps (the rate at which wind energy output increases or decreases) can pose challenges for the grid and its operators. The first step…
This paper proposes a hybrid deep actor-critic framework for the optimal operation of a phase-changing soft open point (PCSOP) in an unbalanced distribution network. The framework combines algorithmic features of off-policy reinforcement learning and imitation learning. The proposed method comprises a policy-guiding module based on the PCSOP physics and an adaptive dynamic experience replay buffer.…
This IEEE paper, ‘Demand Charge Management Based on Battery Aggregation of Commercial and Passenger Electric Fleet Vehicles’, proposes a load management platform to help industrial and commercial electricity customers assess the feasibility of demand charge management through battery aggregation of commercial and passenger EVs. The paper presents a new time-indexed vehicle routing formulation to allow…
Our lab is led by Dr. Hamid Zareipour, an internationally renowned researcher in power grid operation and planning and a highly respected figure in the Alberta’s electricity ecosystem and beyond.
Our team is driven by curiosity and a commitment to transformative research for electricity sustainability, affordability and resilience.
Contact us to discuss how we can help you achieve your energy transformation goals.
