This post summarizes our recent open-access publication in Energies.Read the full article here. Why Focus on Long-Term Load Forecasting? Long-term load forecasting (LTLF) is a crucial tool for planning and operating electric power systems. Accurate forecasts help system operators and planners make informed decisions about investments, operations, and policy. However, LTLF faces major challenges due…
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.…
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…
Wholesale electricity markets are designing market participation models for hybrid resources that consist of energy storage and generation. This IEEE paper investigates the strategy behind two proposed market-participation models of a hybrid resource. The first is co-located hybrid resource, where the solar and energy storage submit separate offers. The second is integrated hybrid resource, where…
Harnessing Grid Integration Opportunities for Accelerated Adoption: This paper proposes a multistage investment planning framework for the fleet transition problem, with a particular focus on the capacity of the electric fleet’s aggregated battery to generate revenue for the fleet owner through self-use or third-party provision of electricity energy services. The proposed approach considers the purchase…
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Price spikes are unexpected and abrupt extreme prices whose value can be several orders of magnitude higher than normal electricity prices. Moreover, due to their stochastic nature, price spikes are short-lived extreme price variations observed in the short-term operation of all…