Abstract
Battery storage, particularly residential battery storage coupled with rooftop photovoltaics (PV), is emerging as an essential component of the smart grid technology mix. However, including battery storage and other flexible resources like electric vehicles and loads with thermal inertia into a probabilistic analysis based on Monte Carlo (MC) simulation is challenging, because their operational profiles are determined by computationally intensive optimization. Additionally, MC analysis requires a large pool of statistically-representative demand profiles to sample from. As a result, the analysis of the network impact of PV-battery systems has attracted little attention in the existing literature. To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks. Specifically, the framework incorporates home energy management (HEM) operational decisions within the MC time series power flow analysis. First, using available smart meter data, we use a Bayesian nonparametric model to generate statistically-representative synthetic demand and PV profiles. Second, a policy function approximation that emulates battery scheduling decisions is used to make the simulation of optimization-based HEM feasible within the MC framework. The efficacy of our method is demonstrated on three representative low-voltage feeders, where the computation time to execute our MC framework is 5% of that when using explicit optimization methods in each MC sample. The assessment results show that uncoordinated battery scheduling has a limited beneficial impact, which is against the conjecture that batteries will serendipitously mitigate the technical problems induced by PV generation.
| Original language | English |
|---|---|
| Article number | 113669 |
| Number of pages | 10 |
| Journal | Applied Energy |
| Volume | 254 |
| DOIs | |
| Publication status | Published - 15 Nov 2019 |
| Externally published | Yes |
Keywords
- Battery storage
- Distributed energy resources
- Home energy management
- Monte Carlo analysis
- Policy function approximation
- Rooftop PV
Cite this
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A novel probabilistic framework to study the impact of photovoltaic-battery systems on low-voltage distribution networks. / Ma, Yiju; Azuatalam, Donald; Power, Thomas; Chapman, Archie C.; Verbič, Gregor.
In: Applied Energy, Vol. 254, 113669, 15.11.2019.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - A novel probabilistic framework to study the impact of photovoltaic-battery systems on low-voltage distribution networks
AU - Ma, Yiju
AU - Azuatalam, Donald
AU - Power, Thomas
AU - Chapman, Archie C.
AU - Verbič, Gregor
PY - 2019/11/15
Y1 - 2019/11/15
N2 - Battery storage, particularly residential battery storage coupled with rooftop photovoltaics (PV), is emerging as an essential component of the smart grid technology mix. However, including battery storage and other flexible resources like electric vehicles and loads with thermal inertia into a probabilistic analysis based on Monte Carlo (MC) simulation is challenging, because their operational profiles are determined by computationally intensive optimization. Additionally, MC analysis requires a large pool of statistically-representative demand profiles to sample from. As a result, the analysis of the network impact of PV-battery systems has attracted little attention in the existing literature. To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks. Specifically, the framework incorporates home energy management (HEM) operational decisions within the MC time series power flow analysis. First, using available smart meter data, we use a Bayesian nonparametric model to generate statistically-representative synthetic demand and PV profiles. Second, a policy function approximation that emulates battery scheduling decisions is used to make the simulation of optimization-based HEM feasible within the MC framework. The efficacy of our method is demonstrated on three representative low-voltage feeders, where the computation time to execute our MC framework is 5% of that when using explicit optimization methods in each MC sample. The assessment results show that uncoordinated battery scheduling has a limited beneficial impact, which is against the conjecture that batteries will serendipitously mitigate the technical problems induced by PV generation.
AB - Battery storage, particularly residential battery storage coupled with rooftop photovoltaics (PV), is emerging as an essential component of the smart grid technology mix. However, including battery storage and other flexible resources like electric vehicles and loads with thermal inertia into a probabilistic analysis based on Monte Carlo (MC) simulation is challenging, because their operational profiles are determined by computationally intensive optimization. Additionally, MC analysis requires a large pool of statistically-representative demand profiles to sample from. As a result, the analysis of the network impact of PV-battery systems has attracted little attention in the existing literature. To fill these knowledge gaps, this paper proposes a novel probabilistic framework to study the impact of PV-battery systems on low-voltage distribution networks. Specifically, the framework incorporates home energy management (HEM) operational decisions within the MC time series power flow analysis. First, using available smart meter data, we use a Bayesian nonparametric model to generate statistically-representative synthetic demand and PV profiles. Second, a policy function approximation that emulates battery scheduling decisions is used to make the simulation of optimization-based HEM feasible within the MC framework. The efficacy of our method is demonstrated on three representative low-voltage feeders, where the computation time to execute our MC framework is 5% of that when using explicit optimization methods in each MC sample. The assessment results show that uncoordinated battery scheduling has a limited beneficial impact, which is against the conjecture that batteries will serendipitously mitigate the technical problems induced by PV generation.
KW - Battery storage
KW - Distributed energy resources
KW - Home energy management
KW - Monte Carlo analysis
KW - Policy function approximation
KW - Rooftop PV
UR - http://www.scopus.com/inward/record.url?scp=85070319937&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2019.113669
DO - 10.1016/j.apenergy.2019.113669
M3 - Article
VL - 254
JO - Applied Energy
JF - Applied Energy
SN - 0306-2619
M1 - 113669
ER -