Abstract
The importance of efficient utilization of biomass as renewable energy in terms of global warming and resource shortages are well known and documented. Biomass gasification is a promising power technology especially for decentralized energy systems. Decisive progress has been made in the gasification technologies development during the last decade. This paper deals with the control and optimization problems for an isolated microgrid combining the renewable energy sources (solar energy and biomass gasification) with a diesel power plant. The control problem of an isolated microgrid is formulated as a Markov decision process and we studied how reinforcement learning can be employed to address this problem to minimize the total system cost. The most economic microgrid configuration was found, and it uses biomass gasification units with an internal combustion engine operating both in single-fuel mode (producer gas) and in dual-fuel mode (diesel fuel and producer gas).
Highlights
Hybrid energy systems development based on renewable energy sources (RES) leads to the need of solving many practical problems, including the problem of optimal power systems’ structure selection and their control.These characteristics of the system depend both on the technical and economic indicators of energy sources, as well as on the availability and energy potential of renewable energy resources in a given area, including the distribution of this potential over time.These problems attract a lot of specialists [1,2,3], including experts in data driven unit commitment problem solvers development
This paper deals with the control and optimization problems for an isolated microgrid combining
The contemporary methods of stochastic online optimization based on reinforcement learning and linear programming were employed, when the microgrids control was based on the Markov decision process (MDP)
Summary
Hybrid energy systems development based on renewable energy sources (RES) leads to the need of solving many practical problems, including the problem of optimal power systems’ structure selection (the ratio of capacities in the energy system of energy sources and storage systems) and their control.These characteristics of the system depend both on the technical and economic indicators of energy sources, as well as on the availability and energy potential of renewable energy resources in a given area, including the distribution of this potential (wind speed and solar radiation intensity) over time.These problems attract a lot of specialists [1,2,3], including experts in data driven unit commitment problem solvers development. Hybrid energy systems development based on renewable energy sources (RES) leads to the need of solving many practical problems, including the problem of optimal power systems’ structure selection (the ratio of capacities in the energy system of energy sources and storage systems) and their control. These characteristics of the system depend both on the technical and economic indicators of energy sources, as well as on the availability and energy potential of renewable energy resources in a given area, including the distribution of this potential (wind speed and solar radiation intensity) over time. The following optimization criteria were employed: energy efficiency, maximum energy production on a specific source of renewable energy, maximum use of installed renewable energy generation capacity, exergy efficiency, minimizing the payback period, minimizing capital costs, environmental impact
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