Does Your Domestic Photovoltaic Energy System Survive Grid Outages?

Abstract

Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer's needs. Next to the usage-specific demand profiles and location-specific production profiles, local energy storage through the use of batteries is becoming increasingly important, since it allows one to balance variations in production and demand, either locally or via the grid. Moreover, local storage can also help to ensure a continuous energy supply in the presence of grid outages, at least for a while. Hybrid Petri net (HPN) models allow one to analyze the effect of different battery management strategies on the continuity of such energy systems in the case of grid outages. The current paper focuses on one of these strategies, the so-called smart strategy, that reserves a certain percentage of the battery capacity to be only used in case of grid outages. Additionally, we introduce a new strategy that makes better use of the reserved backup capacity, by reducing the demand in the presence of a grid outage through a prioritization mechanism. This new strategy, called power-save, only allows the essential (high-priority) demand to draw from the battery during power outages. We show that this new strategy outperforms previously-proposed strategies through a careful analysis of a number of scenarios and for a selection of survivability measures, such as minimum survivability per day, number of survivable hours per day, minimum survivability per year and various survivability quantiles.

Classic and non-prophetic model checking for hybrid Petri nets with stochastic firings

“…Those locations are saved separately per nondeterministic choice (lines 8-12). For every pair of such nondeterministic choices, we traverse the Cartesian product over the resulting two sets of goal states, such that we consider pairs of goal locations Λ c and Λ d (which are reached from different nondeterministic choices; lines [13][14][15][16][17][18][19][20][21][22][23][24][25]. To separate the potential domains Λ c .S and Λ d .S, we then solve the optimisation problem of Equation 5for Λ c and Λ d in line 18, returning the split hyperplanes h 1 and h 2 .…”

“…Therefore, we let the optimisation solver return the corresponding Booleans dir 1 and dir 2 , too. The polytopes P Λ c .S and P Λ d .S are intersected by the half spaces h 1 and h 2 (lines [19][20][21][22], and the latter are stored in splits (lines [23][24]. Since the potential domains are updated, but no probabilities are computed, the recursive call is done for all (conflicting and non-conflicting) child locations in lines 29-33.…”

“…Overall, our algorithms complement a graphical formal modelling framework for a challenging yet highly practical [12,13,23] combination of quantitative aspects by an exhaustive analysis that delivers results with guaranteed coverage and accuracy. We have implemented the non-prophetic algorithm in full generality, and provide a proof of concept of the prophetic variant that supports at most one decision point per pair of concurrently enabled transitions.…”

A Comparative Study between Traditional Backup Generator Systems and Renewable Energy Based Microgrids for Power Resilience Enhancement of a Local Clinic

“…In this paper, the term survivability was used for measuring the resiliency of the system. Survivability was also introduced as the probability of having electricity continuously available during a power outage until it was re-established within t units of time after the interruption of the power grid supply had taken place [28]. For a critical facility such as a clinic, the survivability would typically be 100% for 24 hours of a blackout.…”

Adaptability of photovoltaic mono-polycrystalline solar panels and photovoltaic roof tiles on dwelling roofs of real estate developments

“…These methodologies allow determination of the solar potential in different kinds of architectonic typologies by considering total roof availability in urban areas (Izquierdo et al, 2011). The integration of PV systems into the electrical grid has been analysed in various studies that have determined constraints such as the difference between demand and generation, along with some well-known constraints imposed by the size of the local grids (Wegertseder et al, 2016;Jongerden et al, 2016). These studies evaluate solar potential by considering the total available roof surfaces without considering the effect of the geometry of regular-sloped roofs and the restraints related to the adaptability of solar panels.…”