A spatio-temporality-enabled parallel multi-agent-based real-time dynamic dispatch for hydro-PV-PHS integrated power system

Abstract

Integrated power system has emerged as a powerful alternative to penetrate renewables, due to its ability to reconcile energy discrepancy. However, due to limited mainstreams and complex mountainous meteorology, the dispatch of hydro-photovoltaic-pumped hydro storage (Hydro-PV-PHS) integrated power system (IPS) which are predominantly composed of cascaded daily-regulation and uncontrollable runoff hydropower stations and PVs still miss the expected clean energy utilization rates. To conquer the issue, a novel spatio-temporality-enabled parallel multi-agent-based dynamic dispatch method is proposed. At the outset, a temporal dispatch model fed by dynamic measurements of available PV generation and inflow is presented. To master the uncertainties, such model must be solved in real-time upon the incoming measurements. Whereupon the presented model is recast as Markov decision process for learning dispatch policies, parameterized by neural network agents. To manage the enormous spatial-temporal operating space of the hydro-PV-PHS IPS and to prevent conflict policies, a long short-term memory auto-encoder (LSTM-AE) combined unsupervised learning scheme is used to alleviate divergence and decrease stochasticity of renewables to pre-uncouple policies, which are then distributed to multiple parallel agents. Finally, distributed proximal policy optimization is conducted to produce dispatch policies in an offline parallel manner, with each agent responsible for dispatching the hydro-PV-PHS IPS within the respective operating subspace. The numerical studies in a real-world case demonstrate that the proposed scheme enables real-time and near-optimal dynamic dispatch for the concern IPS, and outperforms other rivals in terms of adaptability, robustness, and efficiency.