# Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities

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## Abstract

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## 1. Introduction

- To analyze different VPP models for different market mechanisms. In this regard, this paper describes different energy market structures and thoroughly explains the role of VPP in each market.
- To address different energy management algorithms within VPP considering various resources such as renewable-based/conventional DGs, battery energy storage, responsive loads, and EVs.
- To compare different planning methods of VPPs in terms of solution methods to optimize VPPs.
- To examine the use of blockchain in the structure of VPPs and the benefits of using this technology.

## 2. VPP Definition

#### Operating Strategies of VPPs

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- In the economic objective, the objective function is to minimize the total costs with respect to less impact on the network. This option may be considered by DG owners or operators. The main limitations in the economic viewpoint are the physical limitations of DGs which may affect the economic dispatch. The impact of VPP on reducing losses cost is because of the elimination of the transmission lines since these resources are close to the load location. In fact, when transmission lines are removed, power is generated near local loads, which can reduce losses. In this case, the network operator can also benefit from this issue. As a result, electric power can be delivered to the customer at a lower price due to reduced losses. Exploiting VPP could also reduce the failure cost and the number of emission pollutants that enter the air; therefore, the cost of these items will be avoided or will be very low.
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- From a technical point of view, the network performance is improved. The purpose of network performance is to minimize power losses and improve voltage fluctuations and network congestion without considering resource costs or revenues. This option is mostly considered by system operators [37].
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- The environmental objective function is considered regardless of the economic or technical aspects and only based on the need for reducing greenhouse gases. This option is fully supported by regulatory schemes.

## 3. Uncertainties in the VPP

#### 3.1. Uncertainty of Renewable Energy Resources

#### 3.2. Market Price Uncertainty

#### 3.3. Load Uncertainty in VPPs

#### 3.4. Modeling Uncertainties

## 4. Energy Management of VPPs

## 5. Planning of VPPs in the Power System

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- MGs can be operated connected to or disconnected from the main grid, while VPPs can only operate in a grid-connected manner.
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- MGs usually require some level of energy storage. However, the presence or absence of storage in VPPs is not very important.
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- MGs depend on hardware changes such as inverters and smart switches, while VPPs heavily depend on smart metering and information technology.
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- MGs include a fixed set of resources in a limited geographic area, while VPPs can combine a wide variety of resources in large geographic areas.
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- MGs are usually traded in retail markets, while VPPs can also be traded in wholesale markets.
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- MGs may face legal and political obstacles, while VPPs could be implemented based on the current structure and legal tariffs.

#### 5.1. Classical Method in Optimal Planning of VPPs

#### 5.2. Heuristic and Meta-Heuristic Methods in VPP Planning

#### 5.3. VPP Planning Methods Based on Learning

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- Policy determines how to deal with each action and how to make decisions in different situations.
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- The reward function determines the goal of the learner function. The purpose of this function is to give a reward for each action of the agent so that the reward increases as the goal gets closer.
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- The model of the RL problem is probabilistic and stochastic, and its states are non-deterministic. For one action, it can go to all states but with one probability.

Algorithm | Advantages | Disadvantages |
---|---|---|

IP and LP | - Simplicity - ease of use - Place of reassessment - Improving the quality of decision-making | - Inability to deal with uncertainties - Necessary need for objective function and constraints |

MILP | - Flexibility in model development - Accurate modeling capability - Convergence to the final solution | - Difficult to understand - Hard implementation of the algorithm - Slow solving speed—high cost for large-scale issues |

PEM | - Low cost - Proper accuracy - Balance between speed and accuracy | - Low accuracy for multidimensional environments - Recognizable pattern of restriction - Weakness in handling samples with high distribution |

PSO | - Easy implementation - Parallel computing capability - Fast and easy convergence - Low computational cost | - Lack of a precise theoretical basis - Instability in solving scattered problems - Convergence to local optimality in complexity problems |

Algorithms based on machine learning | - Fast and cheap generalization - Data analysis - Production and extraction of problem features | - High knowledge is needed for implementation |

## 6. Participation of VPPs in Electricity Markets

- Penetration pricing or pricing to gain market share: Some power companies adopt this pricing policy to penetrate the market and gain a part of the market for a short period of time. These power companies offer some of their services for free or at a low price for selling energy for a limited period of several months.
- Economic price or no low-frill price: The pricing strategies of these products are considered as no low-frill prices where the cost of marketing a product is minimized. Economical pricing is determined for a certain period when the company does not spend more on advertising products and services.
- Using psychological pricing strategies: Psychological pricing strategies are an approach to elicit the consumer’s emotional response rather than their logical response. For example, a company prices its product at $99 instead of $100. The product is priced at $100, making the customer feel that the product is not too expensive. For most consumers, price is a factor in whether or not to buy a product.

#### 6.1. Bilateral Contracts

#### 6.2. VPP in the Day-Ahead Market

#### 6.3. VPP in the Ancillary Services Market

#### 6.4. VPP in the Reserve Market

#### 6.5. Virtual Powerhouse in Daily Markets

Reference | Market Type | Characteristics |
---|---|---|

[87,88] | Day-ahead | Offer to buy and sell energy for every hour of the next day Increasing the flexibility of the power system—high operating profit |

[90,91] | Ancillary services market | Increasing security and reliability of power generation and transmission—the balance of generation and demand at any time |

[94] | Reserve market | Management of excess power generation to ensure supply of demand and security of power supply |

[95] | Daily market | - Adjusting the price of energy trading in the future market—reducing the cost of supply and demand imbalance |

[96] | Real-time market | - Management of power fluctuations between supply and demand and network security |

#### 6.6. VPP in Real-Time Market

## 7. Challenges of Using VPPs

#### 7.1. Challenges of Control and Operation System

#### 7.2. Communication and Information Challenges of VPPs

#### 7.3. Power Exchange Challenges

#### 7.4. Blockchain Applications in VPPs

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- Exchange of electrical energy
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- Effectiveness in responding to the load and checking RESs

## 8. Conclusions and Future Directions

- To use neural networks for VPP in the electricity markets and the overall modeling of these power plants in the power system.
- To propose novel control methods in the context of VPP.
- To present an appropriate protection scheme for VPP.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ADN | Active Distribution Network |

CNN | Convolutional Neural Network |

DR | Demand Response |

DERs | Distributed Energy Resources |

DG | Distributed Generation |

DSO | Distribution System Operator |

EV | Electric Vehicle |

EIA | Energy Information Administration |

EMS | Energy Management System |

EN | Energy Nodes |

FIT | Feed-In Tariff |

IP | Integer Programming |

IoT | Internet of Things |

LP | Linear Programming |

MDP | Markov Decision Process |

MF | Membership Function |

MG | Micro-grid |

MINLP | Mixed Integer Non-Linear Programming |

PSO | Particle Swarm Optimization |

PV | Photovoltaic |

PEM | Point-estimate Method |

Probability Distribution Function | |

RL | Reinforcement Learning |

RES | Renewable Energy Source |

RNN | Recurrent Neural Networks |

TSO | Transmission System Operator |

VPP | Virtual Power Plant |

WT | Wind Turbine |

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Targets | Construction Time | VPP Name | Type of DER | Country/Countries |
---|---|---|---|---|

Development, implementation, and testing of the VPP as well as whether fuel cells can be installed in residential areas | 2001–2005 | VDCPP | Fuel cell | Germany, Netherlands, Spain |

Provide market mechanism | 2005–2007 | PM VPP | $\mathsf{\mu}\mathrm{CHP}$ | Netherlands |

Choosing DER-based systems in order to choose a solution for EU electricity supply with low cost, safety, and high reliability | 2005–2009 | FENIX | $\mathsf{\mu}\mathrm{CHP},$ $\mathrm{PV},\text{}\mathrm{WT}$ | GB, Spain, France |

Providing the balancing power needed to increase the use of wind power | 2009–2012 | EDISON | EVs | Denmark |

Active power supply and small-scale generation | 2010–2013 | FLEX POWER | WT | Denmark |

Implementation of “Smart Distribution” | 2010–2015 | WEB2ENERGY | $\mathrm{CHP},\text{}\mathrm{PV},\text{}\mathrm{WT}$ | Germany, Poland |

Advanced integration of wind turbines | 2012–2015 | TWENTIES | WT | Belgium, Germany, France |

Providing network support services, helping to secure demand, and saving customers’ energy costs | 2016–2018 | SA VPP | PV, battery | Australia |

Reference | Solution Method | Description |
---|---|---|

[56] | MINLP | Presenting a new strategy for providing ancillary energy services |

[57] | MILP | Maximizing profits and minimizing pollutant emissions in VPP |

[58] | LP | Optimum scheduling of VPP with battery regardless of cost |

[59] | MINLP | Planning industrial VPPs |

[60] | LP | Linear programming of market optimization |

[61] | MILP | Optimum planning of day-ahead markets |

[62] | Mathematical programming | Optimal planning of VPP considering battery failure |

[63] | Mathematical programming | Maximum profit in the market and reduction in pollution |

[64] | Monte-Carlo | Optimum planning to increase profit by considering DR |

[65] | MINLP | bi-level planning of VPPs |

[66] | Scenario-based PSO optimization | Reserve planning and VPP energy |

[67] | Point Estimation (PE) | Planning resources in the day-ahead market for VPP |

[68] | Interval optimization | bi-level optimization of VPP |

[69] | PSO | Multi-objective optimization stochastic programming for VPP |

[70] | Combination of genetic and Monte Carlo algorithms | Planning VPP uncertainties |

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**MDPI and ACS Style**

Roozbehani, M.M.; Heydarian-Forushani, E.; Hasanzadeh, S.; Elghali, S.B.
Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities. *Sustainability* **2022**, *14*, 12486.
https://doi.org/10.3390/su141912486

**AMA Style**

Roozbehani MM, Heydarian-Forushani E, Hasanzadeh S, Elghali SB.
Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities. *Sustainability*. 2022; 14(19):12486.
https://doi.org/10.3390/su141912486

**Chicago/Turabian Style**

Roozbehani, Mohammad Mohammadi, Ehsan Heydarian-Forushani, Saeed Hasanzadeh, and Seifeddine Ben Elghali.
2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities" *Sustainability* 14, no. 19: 12486.
https://doi.org/10.3390/su141912486