# A Robust Participation in the Load Following Ancillary Service and Energy Markets for a Virtual Power Plant in Western Australia

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## Abstract

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## 1. Introduction

#### 1.1. Ancillary Services in WEM

#### 1.2. Contributions and Structure

- Developing an expert model for a fast and robust bidding strategy in the LFAS and energy markets, considering PV generation, energy storage scheduling and a gamified contribution of consumers to maximize the profit of the VPP and reduce consumers’ energy costs;
- Analysis of the economic viability of the realistic VPP when participating in the LFAS and energy markets, including the payback period, internal rate of return, cash flow, profit, etc., over the lifetime of the project;
- Comparison of the proposed fast bidding strategy with a traditional robust mathematical approach to show the effectiveness of the proposed strategy for deciding or changing the bidding values in a short period of time.

## 2. Problem Formulation

#### 2.1. The Modelling of Gamification for Customer Engagement

#### 2.2. The Constraints

## 3. A Robust Bidding Strategy for the LFAS and Energy Markets

- The VRFB is dedicated to participating in the LFAS market;
- The excess PV generation is sold to the energy market after covering the customer’s load during PV generation.

#### 3.1. Bidding Model in the LFAS Market

#### 3.2. Bidding Model in Energy Market

#### 3.3. Robustness Consideration

#### 3.4. A Robust Bidding Strategy for the Energy Market Only

## 4. Simulation Results

#### 4.1. Assumptions

#### 4.2. Economic Comparison

#### 4.3. Energy Throughput and Lifetime of VRFB

#### 4.4. Cash Flow Analysis

#### 4.5. The Impact of Gamification

#### 4.6. The Components of the NPV of Revenues and Expenses

#### 4.7. Customer Benefit

#### 4.8. Comparison with a Robust Mathematical Algorithm

## 5. Discussion

- In the electricity market, there is a gate closure moment right before each trading interval [15]. The participants can provide an updated bid for the energy and LFAS markets based on the most recent and up-to-date data and information to maximise their profit. As this period for decision making is very short, many participants cannot effectively use this period due to the higher computational efforts of their bidding algorithm. Therefore, the speed of algorithm needs to be very high to accommodate the need for very quick decision making.Although the error of the proposed method is about 2.7%, the speed of the proposed algorithm (about 1435 times faster) enables the participants to maximise their profit by a better bidding value for each trading interval before the gate closure. If the proposed algorithm improves the bidding for each trading interval by as little as 5% (as an indication) on average, the total benefit of participants due to the use of the proposed algorithm would be higher when compared with the other strategies.
- Sometimes, participants want to analyse uncertainties and different input parameters before the closing gate of each trading interval to find an optimal bid. In such situations, the speed of the algorithm is much more critical. In these cases, the proposed algorithm can attain the opportunity for market participants.
- In long-term power system planning, we need thousands of iterations with thousands of variables. The inclusion of the electricity market in planning and analysis with a bidding strategy that is not fast enough, results in a huge computational effort in the scale of many days. Therefore, the proposed fast bidding strategy is crucial from the planning perspective. Another benefit of the fast algorithm is the much lower memory required for attaining an optimal bid, specifically in the context of planning.

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Glossary

Abbreviations | |

AEMO | Australian Energy Market Operator |

AGC | Automatic Generation Control |

AUD | Australian Dollar |

CAPEX | Capital expenditure |

CRF | Capital recovery factor |

DoD | Depth of discharge |

DSS | Dispatch support service |

HWS | Hot water system |

IRR | Internal rate of return |

LFAS | Load-following ancillary service |

LRRAS | Load rejection reserve ancillary service |

NPV | Net present value |

PV | Photovoltaic |

SD | Standard deviation |

SOC | State of charge |

SRAS | Spinning reserve ancillary service |

SRS | System restart service |

TOU | Time of use tariff |

VPP | Virtual power plant |

VRFB | Vanadium redox flow battery |

WA | Western Australia |

WEM | Wholesale electricity market |

Variables | |

${R}_{tot}$ | Total revenue of the VPP |

${C}_{tot}$ | Total expenses of the VPP |

${R}_{Fix}$ | The fixed revenue |

${R}_{Var}$ | The variable revenue |

${E}_{out}^{y,h}$ | The amount of energy sold to the electricity energy market |

${E}_{RES}^{d,h}$ | The amount of energy sold to customers |

${P}_{LFAS,UP}^{d,h}$ | The bidding power for the upwards LFAS market |

${P}_{LFAS,DOWN}^{d,h}$ | The bidding power for the downwards LFAS market |

${E}_{PV}^{d,h}$ | The amount of energy generated by the PV system |

$SO{C}_{VRFB}^{max}$ | The maximum SOC of VRFB |

${E}_{Energy}^{d,h}$ | The bidding amount in the energy market |

${\pi}^{d,h}$ | The energy market price |

${\tau}_{RES,E}^{d,h}$ | The agreed energy price for selling to customers |

${\zeta}_{LFAS,UP}^{d,h}$ | $\mathrm{The}\mathrm{weighted}\mathrm{average}\mathrm{price}\mathrm{for}{P}_{LFAS,UP}^{d,h}$ |

${\zeta}_{LFAS,DOWN}^{d,h}$ | $\mathrm{The}\mathrm{weighted}\mathrm{average}\mathrm{price}\mathrm{for}{P}_{LFAS,DOWN}^{d,h}$ |

$y,d,h$ | Year, Day, Hour |

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**Figure 1.**An example of LFAS bidding and enablement over four trading intervals. Green: bidding power for upwards LFAS; Blue: bidding power for downwards LFAS; Brown: the enabled power by AGC. Numbers 1 to 4 are representing the trading intervals.

**Figure 4.**The average NPV values of total revenue, expense, and profit with different levels of enablement rate (%) for the LFAS market and without the LFAS market.

**Figure 5.**The payback periods and internal rates of return for different levels of enablement rate (%) for the LFAS market and without the LFAS market.

**Figure 6.**The energy throughput and useful lifetime of VRFB at different levels of enablement rate (%) for the LFAS market.

**Figure 7.**The cash flow for the VPP at different levels of enablement rate (%) when participating in LFAS and energy markets.

**Figure 8.**The total NPV of profit of the VPP project with and without gamification at different levels of enablement rate (%).

**Figure 9.**The payback period of the VPP project with and without gamification at different levels of enablement rate (%).

**Figure 12.**The daily profit comparison between the proposed robust method and the mathematical robust method for participating in the energy market only.

**Figure 13.**The yearly revenue, expense, and profit comparison between the proposed robust method and the mathematical robust method for participating in the energy market only.

PV Generation | Electricity Price | LFAS Price | |
---|---|---|---|

The VPP is selling energy to the energy market | low | low | ----- |

The VPP is buying energy from the energy market | low | high | ----- |

The VPP is participating in LFAS market | ----- | ----- | low |

Parameters | Value | |
---|---|---|

Uncertainty levels (%) | PV generation | 10% |

Electricity price | 10% | |

LFAS price | 20% | |

Gamification parameters | Electricity reduction factor | 50% |

Customer participation | 80% | |

VRFB | Efficiency (%) | 85% |

Maximum energy throughput | 13,000,000 kWh | |

Discount for customers | 10% | |

Interest rate | 5% | |

Horizon year (years) | 20 |

The Proposed Robust Method | The Robust Mathematical Method |
---|---|

0.66 s | 947.10 s |

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## Share and Cite

**MDPI and ACS Style**

Behi, B.; Jennings, P.; Arefi, A.; Azizivahed, A.; Pivrikas, A.; Muyeen, S.M.; Gorjy, A. A Robust Participation in the Load Following Ancillary Service and Energy Markets for a Virtual Power Plant in Western Australia. *Energies* **2023**, *16*, 3054.
https://doi.org/10.3390/en16073054

**AMA Style**

Behi B, Jennings P, Arefi A, Azizivahed A, Pivrikas A, Muyeen SM, Gorjy A. A Robust Participation in the Load Following Ancillary Service and Energy Markets for a Virtual Power Plant in Western Australia. *Energies*. 2023; 16(7):3054.
https://doi.org/10.3390/en16073054

**Chicago/Turabian Style**

Behi, Behnaz, Philip Jennings, Ali Arefi, Ali Azizivahed, Almantas Pivrikas, S. M. Muyeen, and Arian Gorjy. 2023. "A Robust Participation in the Load Following Ancillary Service and Energy Markets for a Virtual Power Plant in Western Australia" *Energies* 16, no. 7: 3054.
https://doi.org/10.3390/en16073054