Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects
- The VPP continues to operate normally in the event of a single user failure, while this problem affects all connected sub-systems of the microgrid.
- The advantage of VPP over microgrids is that the former uses less ESS than the latter. Thus, VPP is less costly to implement and offers a coherent solution.
- For MG implementation, all the resources must be in a geographic area; however, VPP and particularly commercial ones facilitate having a single entity on behalf of a set of resources not related to the same geographic location.
- VPPs use artificial intelligence to develop simple algorithms that ensure optimal production and consumption, unlike microgrids that use very complex optimization algorithms.
- This article discusses the concept of VPP in its entirety. It consists of presenting the different notions from its architecture to the different types of electricity market.
- Classify the different internal control methods and the main optimization algorithms used for each.
- To explain optimization strategies for VPP based on system configuration, parameters, and control techniques.
- To summarize differents markets.
- To give examples of the latest practical implementations of VPP.
2. VPP Concept
2.1. Classification of VPPs
2.1.1. Commercial VPP
2.1.2. Technical VPP
2.2. VPP Grid Services
- Localized clean energy: a VPP unit helps to achieve global warming and pollution. It integrates new technologies and methods that ensure the production of clean energy.
- Real-time demand response: the neural network established between the renewable energy sources (RES) ensures a real-time response to the demand. VPP technology brings all resources’ features and keeps the system in balance.
- Frequency and voltage control: The VPP allows to better manage sudden frequency changes with a fast and efficient response. Moreover, the peak demand is better controlled thanks to the control of the energy flow. It draws the necessary energy from the sources of the nearest neighbors.
- Big data from small sources: Transforming utilities into a digital network can result in high performance by managing the considerable accuracy of the data.
2.3. Internal Control of VPP
2.3.1. Centralized Control Method
2.3.2. Hierarchical Control Method
2.3.3. Comprehensive Control Method
- Centralized VPP Control: The agents’ bidding strategies are coordinated centrally by the VPP to form a final market participation strategy. This alleviates the computationally intensive nature of the centralized control method. In fact, it is distributed among the agents in level II.
- Distributed Agent Control: A local optimization is handled by the distributed agents who submit their operation profile to the VPP for global optimization. As the VPP issues the final coordinated operation profile, all agents proceed with the regional reorganization and execution.
3. Service-Oriented Optimization Algorithms in VPP
3.1. Programming Models
- The robust model offers a better performance from a computational point of view. This asset allows it to be used optimally in the RT decision processes.
- In most cases, the profitability of the robust model is higher than that of the other two models.
- The robust stochastic model is more computationally efficient than the stochastic model, although the latter has a better economic performance.
3.2. Types of Optimization Problems
- Linear Programming (LP);
- Mixed-integer linear programming (MILP);
- Nonlinear programming (NLP);
- Mixed-integer nonlinear programming (MINLP).
3.2.1. Mathematical Methods
- Fuzzy algorithm: Specially used in wind energy processes. In , the fuzzy logic algorithm has been demonstrated to increase prediction accuracy. The authors used a stochastic model to consider the uncertainty of renewable generations and market prices. An iterative procedure has been used in  based on the zone-based observation and focusing algorithm, which is divided into two parts. The first part assigns optimal solution area determination, while the second part associates with a local search to obtain the optimal solution. A local search is performed in a second step to obtain the optimal solution. The possibility of obtaining a local optimum with this approach is minimal. Other studies such as [46,54] have also used the same algorithm.
- The authors of the articles [37,73] use a branch-and-bound system that guarantees an intelligent hunt for the optimal outcome. It consists of evaluating the different options based on the value of integer variables, then excluding the combinations that do not respect certain constraints, and finally determining the optimal conditions according to their limits. It facilitates the convergence to the global optimum of the problem, since it has different strategies to explore the field of results. Therefore, its advantage is to limit significantly the search for the optimum. Nevertheless, the major disadvantage of this system is that it is memory intensive, since each possible result must be independent, so it must contain all the information for the branching process. This also makes it impossible to solve a global structure to obtain the result.
- The authors in  decomposed the PPV auction problem into different power demands by using dynamic scheduling. This approach demonstrated good practicability for rebalancing responses to intraday demands with short continuation times. The system has advantages: the first is its ability to handle separate variables, constraints, and queries at the level of each subproblem rather than considering all aspects simultaneously in a full decision model. The second is its ability to increase the efficiency of the resolution by avoiding repeating the exact calculation several times.
3.2.2. Heuristic Methods
3.2.3. Summary Methods
3.3. Multiobjective Optimization Algorithm
3.4. Distributed Optimization Algorithm
- Iterative method based on information exchange : This method involves a centralized information coordinator and a small number of regional controllers. These controllers make their decisions individually after receiving the incentive or control signals from the information coordinator. After iterations between the two, the final decisions of the regional controllers converge based on specific criteria.
- Game theory method : The Nash equilibrium is achieved in a fully distributed manner in this method. Participants adopt tactical or selfish strategies, and they are free to cooperate or not to cooperate.
- Auction-based method : In this method, participants can exchange energy in both directions according to the established rules. To solve the trust problem between the participants, blockchain technology is implemented, and a sensible smart contract is required.
4. Electricity Markets
4.1. Futures and Forward Market
4.2. Bilateral Contracts
4.3. Day-Ahead Market
4.4. Reserve Market
4.5. Intraday Market
4.6. Real-Time Market
5. Practical Implementation of a Virtual Power Plant
6. Conclusions and Future Directions
- In order to achieve optimal control and coordination between components and thus maximize operating profit, researchers have focused on developing VPP models. There is a wide variety of these models.
- The models developed are becoming complete and complex and include more operating constraints. Moreover, more advanced optimization techniques are required to reach an optimal solution.
- The decentralized generation in the VPP has contributed to more active participation in different types of markets; we have noticed the inclusion of bilateral contracts, forward contracts, balancing markets, and the day-ahead spot market.
- The proposed models have rarely been applied to real cases, as in industrial processes that require the management of electricity consumption and its production facilities.
Conflicts of Interest
|VPP||Virtual Power Plant|
|TVPP||Technichal Virtual Power Plant|
|CVPP||Commercial Virtual Power Plant|
|DERs||Distributed Energy Resources|
|RES||Renewable Energy Resources|
|DSO||Distribution System Operator|
|TSO||Transmission System Operator|
|ISO||Independent System Operator|
|WT||Large-Scale Wind Turbine|
|PV||Centralized Photovoltaic Station|
|RBM||Real-Time Balancing Market|
|ASM||Ancillary Service Market|
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|Proposed Model||Optimization’s Goal||Contributions||References|
|Stochastic programming and robust optimization||Maximize the VPP profit||Combination of bilateral contracts and the DA market||[31,41,42,43]|
|Static robust optimisation||Deal with uncertain wind power production and market prices||Proposed stochastic adaptive robust mixed integer linear programming||[17,30,44,45]|
|Robust stochastic models||Improve the prediction accuracy||Energy management method that controls the model prediction by modifying the time scale so as to significantly improve the prediction accuracy||[46,47,48]|
|Stochastic hybrid ıntelligent algorithm||Prediction of energy storage unit’s level||Balanced energy and cheaper priced electricity have been obtained||[49,50,51]|
|Optimization Problem||Optimization’s Goal||Contributions||References|
|An adaptive robust approach MILP||The optimal DA (or RT) energy, reserve dispatch and the worst-case realization of uncertain DA (or RT) market energy prices||Achievable for all possible cases of the considered uncertainties within a confidence limit and also optimal for the worst case realization of these uncertainties||[18,29,44]|
|Mixed linear integer method||Forecasting wind speed and solar radiation||By using a prediction algorithms, occured faults can be detected by comparing obtained results with power generation||[12,13,16]|
|CPLEX (IBM Log Optimisation Studio) program based on mixed-integer linear programming||Results of real-time integration of DER into VPP||Optimal real-time integration of VPP improves economic feasibility and produces reliable and functional power. Research studies are being conducted on how grid extension and the grid can affect feasibility and performance.|||
|Solving Method||Optimization’s Goal||Contributions||References|
|The fuzzy logic algorithm has been used for improved prediction accuracy||Used especially in wind energy processes||The proposed stochastic model used for renewables ressource (wind energy) and their market price||[46,53,54]|
|Monte Carlo simulation method||PV radiation prediction (if the problem cannot be solved by mathematic or physical method, it is digitized with repeated random sampling)||Simulation results demonstrate that prediction accuracy increased||[47,48,55]|
|Empirical mode and artificial neural network (ANN) decomposition||Combination of traditional wind turbine fault diagnosis algorithms||Forecasting results have been refined over conventional methods|||
|ANN algorithm||This algorithm is mainly used for an interconnected system||It improves computing efficiency and predictions are more precise||[33,49,56]|
|A geographic routine algorithm based on ant colony optimization||Study of density/efficiency and performance trade-offs||The denser the network, the better the performance up to the saturation limit||[57,58,59]|
|Genetic and adaptive heuristic search algorithm (based on the evolutionary idea of natural selection)||Multiple DERs reliability problem solving||Optimal sizing using a genetic algorithm||[60,61,62]|
|Firefly algorithms (FFA) are inspired by firefly that creates a mathematical equation of these behaviors)||Optimization of energy flow, transmission and distribution lines. Create a flexible change of lines according to their efficiencies||An electrical system based on the FFA of choosing the most efficient line||[50,51,63,64]|
|Artificial bee colony||DER placement and sizing process||Placement and sizing of DERs in an electrical system||[65,66,67]|
|Quantum PSO (MSC quantum particle swarm optimization (QPSO), based on quantum behavior)||To change updating strategy and obtain high searching accuracy||Authors proposed an improved model of traditional PSO||[34,68,69]|
|Particle swarm algorithm||Considering the uncertainty in the optimal energy management||A probabilistic framework for management of microgrid||[70,71,72]|
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Nafkha-Tayari, W.; Ben Elghali, S.; Heydarian-Forushani, E.; Benbouzid, M. Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects. Energies 2022, 15, 3607. https://doi.org/10.3390/en15103607
Nafkha-Tayari W, Ben Elghali S, Heydarian-Forushani E, Benbouzid M. Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects. Energies. 2022; 15(10):3607. https://doi.org/10.3390/en15103607Chicago/Turabian Style
Nafkha-Tayari, Wafa, Seifeddine Ben Elghali, Ehsan Heydarian-Forushani, and Mohamed Benbouzid. 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects" Energies 15, no. 10: 3607. https://doi.org/10.3390/en15103607