# A Single-Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting in the Malaysian Electricity Supply Industry

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## Abstract

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## 1. Introduction

- (1)
- It presents a novel forecasting formulation model for the ICPT regime in Malaysia, where the accuracy of the model has been configured and tested with time series and machine learning techniques.
- (2)
- It provides a comprehensive analysis of the data collected from the real generation system while putting forward much widespread discussion on the three-baseline model for the ICPT’s main component.
- (3)
- It contributes to providing valuable forecasting ICPT price information for electricity consumers in Peninsular Malaysia, where the sustainable electricity market can be enhanced significantly.

## 2. Related Previous Work on Forecasting Models

## 3. Formulation of ICPT

_{P}is the average fuel cost for regulatory period ‘P’ (cent/kWh). Meanwhile, m represents the month in which the ICPT adjustment takes effect; C

_{m}is the estimated total fuel cost for month ‘m’ (MYR); D

_{m}is the actual total fuel cost for month ‘m’ (MYR); F

_{m}is the estimated total qualifying sales (kWh); and E

_{m}is the audited total qualifying sales for month ‘m’ (kWh). FFUL

_{S}is the forecasted fuel cost for the six-month period ‘s’ (MYR); WACC

_{P}is the allowed weighted average cost of the capital of RBE for the regulatory period ‘P’ (%); and FSAL

_{t}is the forecasted total electricity sales in the year ‘t’ as made at the time of setting the base average tariff (in kWh).

_{S}demonstrates the six-month period ‘s’ (cent/kWh). AGSC

_{P}is the average other generation cost for the regulatory period ‘P’ (cent/kWh) that finally contributes to the second fuel cost and other generation costs’ pass-through adjustment in the six-month period (cent/kWh), A

_{1}and B

_{1}, congruently. Meanwhile, G

_{m}and H

_{m}represent the estimated total other generation cost for month m (MYR) and the actual total other generation cost for month m (MYR), respectively. On the other hand, for Part 2 of Equation (2), the details of ${F}_{x}$ can be expressed by the following Equation (12):

_{m}is an approved payment from the Electricity Industry Fund (EIF) to the single buyer, related to the ICPT adjustment, for month m (MYR), while FUNP

_{m}is the payment by the single buyer into the EIF, related to the ICPT adjustment for month m (MYR). The calculation result was divided to estimated the total qualifying sales (kWh), F

_{m}, accordingly. Basically, the value of F

_{x}was considered fixed during the adjustment but really depends on the banking announcement, especially on the overnight policy rate (OPR) values.

## 4. Method

#### 4.1. Forecasting Formulation

- (1)
- Autoregression (AR): A regression analysis is used to compare the time series to its prior values, such as y(t − 1), y(t − 2), etc. The letter p stands for the lag order.
- (2)
- Integration (I): Differencing is used to make the time series stationary. The difference’s order is indicated by the letter d.
- (3)
- Moving average (MA): Regression is performed on the time series using residuals from previous observations, such as the error ε(t−1), the error ε(t−2), etc. The error lag order is indicated by the letter q. In the equation above, y^′ is the differenced series, ϕ1 is the first AR term’s coefficient, p is the AR term’s order, θ11 is the first MA term’s coefficient, q is the MA term’s order, and εt is the error.

#### 4.2. Dataset Collection

#### 4.3. Implementation of Techniques

- (1)
- The initial step in calculating the ICPT involves determining the interim fuel cost pass through adjusting for a six-month period (IFUC
_{S}) using Equation (6). The estimated and actual total fuel costs (C_{m}, D_{m}) were derived from the projected fuel cost using the ARIMA and LSSVM models. The data for D_{m}were not accessible due to limited resources. As a result, forecasted data were utilised instead. The estimated total qualifying sales (F_{m}) were acquired from the websites of single buyers (SBs). The audited total qualifying sales, to which the ICPT adjustment was applied, were obtained from the Grid System Operator (GSO) website. By using Equation (5), the average fuel cost was calculated. The total forecasted fuel cost (FFULs) for six months was derived from the previously forecasted fuel cost. The weighted average cost of capital for Regulatory Period 3 (RP3) was set by the government at 7.3%. The forecasted total electricity sales in the year 2021 as made at the time of setting the base average tariff were obtained from the SBs. - (2)
- Then, using Equation (6), the first fuel cost pass-through adjustment (As) was determined.
- (3)
- Next, Equation (7) was used to determine the interim of the other generation cost pass-through adjustment (IGSCs). System marginal pricing (SMP) on SB websites was used to determine the estimated and actual total other generation costs (G
_{m}, H_{m}). - (4)
- To determine the average other generation cost (AGSCs) using Equation (8), the forecasted other generation cost was obtained by subtracting the generation margin (G
_{m}) from the forecasted fuel and fuel-related costs (FFULs). - (5)
- Equation (7) is then used to compute the first other generation cost pass-through adjustment (Bs) in the six-month period.
- (6)
- The next part involves calculating the secondary fuel and additional generation cost pass-through adjustment within the designated six-month timeframe. This can be achieved by utilising Equations (10) and (11).
- (7)
- The remuneration rates for the ICPT adjustment, specifically IARRs-1 and IARRs-2, are constantly set at 2.8738 and 2.86, respectively.
- (8)
- The six-month generation cost adjustment was determined using Equation (3).
- (9)
- Equation (12) was used to compute the fund contribution (FUNDs). The approved payment (FUNP
_{m}) from the Electricity Industry Fund (EIF) and the payment by the single buyer (FUNT_{m}) into the EIF are fixed at MYR 1.6 billion and MYR 1.3 billion, respectively. - (10)
- The ICPT price was then determined using Equation (2).

## 5. Results and Discussion

#### 5.1. Moving Average (MA) Forecasting Profile

#### 5.2. LSSVM Forecasting Profile

#### 5.3. ARIMA Forecasting Profile

#### 5.4. Analysis of the MAPE

#### 5.5. Discussion of the Forecast ICPT

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The general description of the MESI structure in Peninsular Malaysia comes with two types of tariffs, which are the ICPT and the base tariff, under the IBR.

Condition | Description |

r = +1 | linear and perfect positive correlation |

0.8 < r < 1.0 | very strong linear correlation |

0.6 < r < 0.8 | strong linear correlation |

0.4 < r < 0.6 | moderate linear correlation |

0.2 < r < 0.4 | weak linear correlation |

r = 0 | no correlation exists between the two variables |

Year | Fuel Type | MAPE (%) | ||

MA | ARIMA | LSSVM | ||

2022 | Coal | 29.79 | 1.63 | 26.28 |

2022 | Gas | 32.94 | 5.47 | 34.46 |

2022 | LNG | 39.78 | 5.09 | 36.90 |

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## Share and Cite

**MDPI and ACS Style**

Khairuddin, F.K.; Zaini, F.A.; Sulaima, M.F.; Shamsudin, N.H.; Abu Hanifah, M.S.
A Single-Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting in the Malaysian Electricity Supply Industry. *Electricity* **2024**, *5*, 295-312.
https://doi.org/10.3390/electricity5020015

**AMA Style**

Khairuddin FK, Zaini FA, Sulaima MF, Shamsudin NH, Abu Hanifah MS.
A Single-Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting in the Malaysian Electricity Supply Industry. *Electricity*. 2024; 5(2):295-312.
https://doi.org/10.3390/electricity5020015

**Chicago/Turabian Style**

Khairuddin, Fatin Khairunnisa, Farah Anishah Zaini, Mohamad Fani Sulaima, Nur Hazahsha Shamsudin, and Mohd Shahrin Abu Hanifah.
2024. "A Single-Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting in the Malaysian Electricity Supply Industry" *Electricity* 5, no. 2: 295-312.
https://doi.org/10.3390/electricity5020015