Evaluation of a multi-stage stochastic optimisation framework for energy management of residential PV-storage systems

Abstract

In Australia, the penetration of rooftop photovoltaic (PV) systems with storage is expected to increase in the future because of rising electricity costs, decreasing capital costs and growing concerns about climate change. Residential energy users can seize the full financial benefits of these systems by using an automated energy management system (EMS) to schedule and coordinate their energy use. An important aspect of an effective EMS is to control the battery state of charge, taking into consideration of the intermittent nature of PV generation and variability of electrical demand over a decision horizon of several days. However, this is difficult because of the computational burden associated with the currently proposed solution techniques. Given these existing shortcomings, this paper evaluates a two-stage stochastic optimisation framework for energy management of residential PV-storage systems to identify the benefits of having a longer decision horizon. That is: a simplified longer-horizon solver that uses stochastic mixed-integer linear programming (MILP) and a more detail shorter horizon solver using dynamic programming. In doing so, this paper discusses the general benefits of residential PV-storage systems coupled with an EMS.Index Terms-future grid, renewable energy sources, demand response, PV-storage systems, home energy management, stochastic mixed-integer linear programming, dynamic programming.

Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

“…On the contrary, authors in [8,126,127] used nonlinear efficiency curves both for the battery and the inverter, since, in reality, efficiencies are not constant. For batteries, they depend on many factors like SOC, temperature, charge and discharge rates, etc.…”

Review of residential PV-storage architectures

“…An optimization performed for the day ahead was presented in [46] using forecast and a quadratic program based minimization. Multiple optimization techniques [47] have been reported, for instance: multi-stage stochastic optimization [48], Lyapunov approach [49], particle swarm based, and fuzzy logic [50]. Additionally, to optimize the cyclic operation of battery, genetic algorithms have been presented in [51].…”

A Fast and Optimal Smart Home Energy Management System: State-Space Approximate Dynamic Programming

“…This underlying optimization problem can be solved using deterministic and stochastic mixed-integer linear programming (MILP) [1]- [9], particle swarm optimization (PSO) [10]- [14], dynamic programming (DP) [9], [15]- [17], approximate dynamic programming (ADP) [15], [16], [18]- [23] and policy function approximations using machine learning [24]. Solving the day-ahead planning problem using MILP and PSO means that we have to either resolve a difficult optimization problem during the real-time decision making process when uncertainties regarding PV, load and price arise, which will require a high computational power.…”