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Background
Aims and Objectives
Methodology
• Buildings with installed solar panels and batteries can store electricity for personal
consumption or trade to the national grid.
• To design a ‘smart controller’ that computes the optimal amount of electricity to be traded to
the grid.
• To calculate the optimal electricity amount based on the input weather forecast and domestic
power consumption within specified battery constraints and process model.
• To use model predictive control (an advanced control approach) to design the controller and
Matlab® software to simulate the computation.
Sub-heading: Arial font, italic 30pt
Theoretical Approach
• There are five input parameters of an MPC controller to be specified (Figure 1).
• With these inputs, the MPC will predict the future value based on current value and optimize
control action accordingly.
• For the next time step, the MPC will sample a new process state and the new output values will
be calculated via the same algorithm. The iteration continues to compute an optimal solution.
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Results & Discussions
Figure 1: Computation algorithm of Model Predictive Control
Figure 3: The disturbance signal input
(power generation + demand)
Conclusion • The MPC approach is capable of computing the optimal amount of electricity needed to be
traded to the grid to achieve the specified constraints and control objectives.
• MPC controller with measured disturbance (Case 1) provides better performance and higher
computational accuracy compared to the one without measured disturbance (Case 2).
Future Work • Designing an economic MPC controller which gives better optimisation in terms of economical
performance.
• Including fluctuating electricity price as another input data to provide more accurate computation
for revenue optimisation.
• Establishing networking among buildings with both solar cells and batteries for electricity
trading.
Figure 4: The battery energy level signal output
(a) with measured disturbance (b) without measured disturbance.
• Input data:
Power generated on 1-minute basis by a 12 m2 domestic solar cells with 15% efficiency
Domestic power consumption on 1-hour basis.
• The input data represent the disturbance parameter (Figure 3).
• Real data obtained from Australian Government Bureau of Meteorology.
• Objective function:
• Specified parameters:
- Range of manipulated variable (-10 kW [max buying] – +20 kW [max selling])
- Battery capacity (physical constraint: 0 – 0.5 kWh)
- Nominal battery’s energy content (set point: 0.333 kWh)
• Controller tuning – to obtain more accurate optimal solution. This is done by changing:
- Prediction horizon (longer horizon increases prediction accuracy)
- Control horizon (longer horizon enhances better control action)
- Weighting coefficient of process output (battery’s energy level) and manipulated variable
(Higher coefficient produce greater control action)
Figure 2: Summary of the methodology used to perform the simulation
(a) (b)
Figure 5: The optimal solution for manipulated variable signal output
(a) with measured disturbance (b) without measured disturbance.
(a) (b)
• Case 1: The battery energy level contains no fluctuation Figure 4(a)
The optimal solution is less noisy (little fluctuation) Figure 5(a)
• Case 2: The battery energy level contains fluctuations Figure 4(b)
The optimal solution is more noisy (high fluctuation) Figure 5(b)
• The nett amount of electricity traded was computed from the area under the curve in Figure 5
(a) and (b). This was done to calculate the electricity cost.
Case 1: AUD 9.53
Case 2: AUD 9.61
Both values are positive, indicating that revenue was generated
• The revenue generated in Case 2 (without measured disturbance) is greater than Case 1
(with measured disturbance). But Case 1 produce more accurate results due to less noisy
signal and followed the specified constraint.
Distributed Electricity Storage Optimisation:
A Model Predictive Control Approach Author: Sheikh Mohammad Faisal Sh. Mohd. Nasir
Supervisor: Prof. Jie Bao & Dr. Michael J. Tippett
Research Theme: Fundamental and Enabling Research
The Problem • The constantly changing weather, electricity demand and price hinder correct decision
making: how much electricity needs to be traded (i.e. buy and sell) such that demand is
fulfilled and generated electricity is utilised while maximising revenue?
• Constraint:
Battery: 0 kWh – 10 kWh
Amount of electricity to be traded: -5 kW (maximum buying) – +5 kW (maximum selling)
• The MPC application in Matlab® was used to design the controller.
• Two cases were compared to show the need for forecasting and prediction:
Case 1: MPC controller with measured disturbance
Case 2: MPC controller without measured disturbance
• Controller tuning was employed to obtain more accurate optimal solution.
600
0
2
2
0
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t
ydt
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Set point
Constraint
Signal
[1]
Reference [1] Goodwin, G., Graebe, S., & Salgado, M. (2001). Control System Design. Michigan: Prentice Hall
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