Virtual power plants (VPP) is a network of medium and decentralized power producing units such as solar parks, wind farms, combined heat and power units (CHP), and also storage systems such as batteries, and flexible power consumers. In VPP, power units are interconnected and are solely dispatched from a standard central control room, but each group retains its independence in ownership and operation. The main aim of the virtual power plants is to relieve the grid of the power load, which is done by distributing the energy produced by an individual power unit at times of peak load. Besides, the combined power consumption and power production of each unit networked under the virtual power plant is dealt with on the energy exchange market.
VPP integrates numerous power sources to give a reliable assurance on their power supply and is best suited for extreme power needs such as during periods of peak load electricity and energy load due to short notices.
A central control system is the technology behind the working of VPP. As said, a virtual power plant involves networked power producing units, and the members of the VPP are linked with the virtual power plant control system through a remote-controlled unit. Through this connection, all resources can be effectively controlled, monitored, and coordinated by the central control system. In addition to resource control, the system facilitates a secure flow of data and other control commands, a fate achieved by utilizing encryption protocols that protect data from traffic. The central control system, like any other power plants through the use of some unique algorithms help to strike a balance in reserve commands coming from the transmission system operator.
There needs to be a clear flow of data between the VPP and the individual power generating units, and VPP facilitates this through the remote-controlled central control system. Through the system, there is a real-time provision of data on the capacity utilization of every power producing units. For instance, information on electricity storage charge level, consumption data, or data on solar plant and wind energy feed-ins all of which can be utilized to guide in electricity trade forecasting or scheduling control for a specific power generating plant.
The aims of a VPP is subject to its operation market. But generally, virtual power plants are meant to network distributable energy assets, which mainly include renewable sources of energy such as hydropower, wind, biomass, and solar as well as energy storage systems and flexible power consumers, which can also be referred to as demand-side management or demand response. The VPP is to forecast, monitor, dispatch, and optimize the consumption and generation of these energy resources.
It is VPP that ensures efficiency in energy resources. Through the VPP from where different energy resources, whether the distributed energy asset, energy storage system, or demand site management, are aggregated and, therefore, these assets can be effectively traded, optimized, monitored, and even forecasted as a single power system. This way, efficiency and reliability in renewable energy can be boosted by striking a balance between power consumption and power generation by considering all the variables.
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Another objective of the Virtual power plant is the integration of renewable sources of energy into the current energy market. Because individual power plants may not meet all the market demands because of the vast market requirements for energy reliability and availability, VPP combines different energy firms and thereby able to trade in a big market and serve those with high energy needs such as industrial consumers. In other terms, VPP serves as an intermediary between the wholesale energy market and the DERs and trades energy on behalf of the DER owners because, by themselves, these owners cannot participate fairly in the market.
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VPP applies several risk management strategies. Mostly, they incorporate five different risk hedging approaches, that is Second-order Stochastic Dominance (SSD), First-order Stochastic Dominance (FSD), Information Gap Decision Theory (IGDT), Conditional Value at Risk (CVaR), and Robust optimization (RO) in their decision-making processes. Some of the critical decisions that require robust risk analysis include decisions on the future electricity market, decisions on bilateral contracts, and those touching on the exchange market for derivatives.