Stochastic Optimal Control Problems for the Cost-Optimal Management of a Standalone Microgrid

TL;DR

This paper formulates and solves a stochastic optimal control problem for managing a standalone microgrid with renewable energy, batteries, and fuel, focusing on cost minimization under uncertainties and capacity constraints.

Contribution

It introduces a novel finite fuel stochastic control model for microgrid management, incorporating renewable sources, battery limits, and demand penalties.

Findings

Optimal control policies reduce expected costs

Effective handling of renewable variability and demand uncertainty

Demonstrated numerical solutions using Markov decision processes

Abstract

In this paper, we consider a domestic standalone microgrid equipped with local renewable energy generation such as photovoltaic panels, consumption units, and battery storage to balance supply and demand and investigate the stochastic optimal control problem for its cost-optimal management. As a special feature, the manager does not have access to the power grid but has a local generator, making it possible to produce energy using fuel when needed. Such systems are very important for rural electrification, particularly in developing countries. However, these systems are very complex to control due to uncertainties in the weather and environmental conditions, which affect the energy generation and the energy demand. In addition, we assume that the battery and the fuel tank have limited capacities and that the fuel tank can only be filled once at the beginning of the planning period. This leads us to the so-called finite fuel problem. In addition, we allow the energy demand to not always be satisfied, and we impose penalties on unsatisfied demand, the so-called discomfort cost. The main goal is to minimize the expected aggregated cost of generating power using the generator and operating the system. This leads to a mathematical optimization problem. The problem is formulated as a discrete-time stochastic control problem and solved numerically using methods from the theory of Markov decision processes.