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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.

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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.

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Reference [1] Goodwin, G., Graebe, S., & Salgado, M. (2001). Control System Design. Michigan: Prentice Hall