Heuristic Optimization for Microload
Shedding in Generation Constrained Power Systems
Abstract
While the causes of power system outages are often complex and multi-faceted,
an apparent deficit in generation compared to a known demand for electricity could be
more alarming. A sudden hike in demand at any given time may ultimately result in the
total failure of an electricity network. In this paper, algorithms to efficiently allocate the
available generation is investigated. Dynamic programming based algorithms are
developed to achieve this constraint by uniquely controlling home appliances to reduce
the overall demands for electricity by the consumers on the grid in context. To achieve
this, heuristic optimization method (HOM) based on the consumers’ comfort and the
benefits to the electricity utility is proposed. This is then validated by simulating
microload management in generation constrained power systems. Three techniques;
General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding
(ERS) techniques were studied for effecting efficient microload shedding. The research
is aimed at reducing the burden imposed on the consumers in a generation constrained
power system by the traditional load shedding approach. Additionally, the reduction of
the excess curtailment is a prime objective in this paper as it helps the utility companies
to reduce wastage and ultimately reduce losses resulting from over shedding. Reducing
the peak-to-average ratios (PAR) on the entire network in context as a critical factor in
the determination of the efficiency of an electricity network is also investigated. In the
long run, the PAR affects the price charged to the final consumer. Simulation results
show the associated benefits that include effectiveness, deployability, and scalability
of the proposed HOM to reduce these burdens.
| Original language | English |
|---|---|
| Pages (from-to) | 13294-13304 |
| Number of pages | 11 |
| Journal | IEEE Access |
| Volume | 8 |
| Publication status | Published - 10 Jan 2020 |
All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon
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