Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning

The integration of pipeline energy storage in the control of a district heating system can lead to profit gain, for example by adjusting the electricity production of a combined heat and power (CHP) unit to the fluctuating electricity price. The uncertainty from the environment, the computational complexity of an accurate model, and the scarcity...

Distributed coordination of deferrable loads: A real-time market with self-fulfilling forecasts

Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi...

Forecasting design and decision paths in ship design using the ship-centric Markov decision process model

A Monte Carlo approach to the ship-centric Markov decision process for analyzing decisions over converting a containership to LNG power

A Monte Carlo approach to the ship-centric Markov decision process (SC-MDP) is presented for analyzing whether a container ship should convert to LNG power in the face of evolving Emission Control Area regulations. The SC-MDP model was originally developed as a means to analyze uncertain, sequential decision making problems. However, the...