Utility-Agnostic Virtual Power Plant Operation: Architecture, Challenges, and Essential Features

The operating income of power supply virtual power plants is greatly affected by factors such as weather, external electricity prices and popular policy mechanisms. How to sort out and analyze the factors affecting the participation of power supply virtual power plants in the market operation and how to enhance the flexibility of virtual power plant operation decision becomes an urgent problem to be solved. This paper proposes a system dynamics-based approach to analyze the factors influencing the market operation of power supply virtual power plants. First, a system dynamics model of power supply virtual power plants participating in the day-ahead market is built, dividing the distributed generation (DG) module and the day-ahead market module. Then analyze the impact of weather and real-time electricity prices on the short-term operation of virtual power plants. On the basis of this, a system dynamics model of the long-term market for power supply virtual power plants is built, taking into account the impact of the green certificate trading mechanism and carbon trading mechanism on the long-term operation of virtual power plants. By simulating the impact of different factors and policy mechanisms on the strategies of power supply virtual power plants participating in the day-ahead market and long-term market through arithmetic examples, we provide a theoretical basis for the adjustment of bidding strategies of power supply virtual power plants participating in the market and constructive suggestions for the promotion of virtual power plants and different policy mechanisms.

The distributed generation of new energy can effectively use the local endemic clean new energy and provide green power. However, due to the small scale and scattered layout of distributed generation resources, it is difficult to participate in the economic dispatch of power systems and even the competition of the power market. Virtual Power Plant (VPP) can act as a carrier of Distributed Energy Resource (DER) to manage its internal energy, to carry out combined bidding in the day‐ahead energy market and the regulation market. When virtual power plants coordinate multiple distributed energy resources to participate in electricity market transactions, the degree of disclosure of their internal privacy information will produce different trading strategies. A Stackelberg game model between multiple virtual power plant aggregators and virtual power plant operators is constructed, and the Kriging model is combined to protect the privacy information in the transactions of virtual power plant operators. Energy management for distributed generation is carried out, and the profit situation of virtual power plant operators under different strategies is analyzed.

Economic optimization scheduling of virtual power plants considering an incentive based tiered carbon price

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a virtual nuclear power plant operator. The virtual operator can follow procedures the timing and reliability of their actions are tied to dynamic human performance modeling parameters (Boring et al., 2016). Unlike traditional (static) risk modeling HUNTER has a dynamic version of SPAR-H to calculate performance shaping factors (PSFs) based on evolving plant conditions. HUNTER models task level Goals-Operators-Methods-Selection rules (GOMS)-HRA as the operator walks through procedure steps. Here we describe how the HUNTER virtual operator model was tightly coupled with the Rancor Nuclear Power Plant Microworld as part of a suite of probabilistic risk assessment (PRA) tools.

The emergence of the shared energy storage mode provides a solution for promoting renewable energy utilization. However, how establishing a multi‐agent optimal operation model in dealing with benefit distribution under the shared energy storage is still a challenge. Considering the multi‐agent integrated virtual power plant (VPP) taking part in the electricity market, an energy trading model based on the sharing mechanism is proposed to explore the effect of the shared energy storage on multiple virtual power plants (MVPPs). To analyse the relationship among MVPPs in the shared energy storage system (SESS), a game‐theoretic method is introduced to simulate the bidding behaviour of VPP. Furthermore, the benefit distribution problem of the virtual power plant operator (VPPO) is formulated based on the Nash bargaining theory. In the case study, the proposed method is conducted in four VPPs with different resource endowments in terms of techno‐economic and operation efficiency. Results verify that the multiple virtual power plants with a shared energy storage system interconnection system based on the sharing mechanism not only can achieve a win‐win situation between the VPPO and the SESS on an operation cost but also obtain the optimal allocation scheme and improves the operation efficiency of the VPPs.

The virtual power plant consisting of a large-scale energy storage system and a controllable energy source can reduce the potential safety hazards caused by the unstable output power of new energy when it is connected to the grid, thereby increasing the reliability of power supply. The energy storage system cooperates with the distributed photovoltaic and gas turbine to ensure that the virtual power plant participates in the system demand response, reduces the cost of power generation, and obtains more revenue. To this end, a virtual power plant operation strategy was designed. With the objective of maximizing the net profit of the virtual power plant in each period, a virtual power plant economic optimization configuration model was constructed, and a single-target particle swarm optimization algorithm was used to obtain the output of the energy storage system in the virtual power plant, and the signals are distributed to supercapacitors, lithium titanate batteries, and all-vanadium redox batteries through Fourier transform to realize the configuration of the energy storage system capacity and power.

A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response

With the increasing penetration of clean energy sources such as wind power and photovoltaic in the grid, the volatility, intermittency, and randomness of their power output have impacted the safe and stable operation of the grid. To address the challenges posed by scheduling and the potential wastage of renewable energy due to these factors, a two-layer optimal scheduling model for a virtual power plant that takes into account source-load synergy is proposed in this paper. In the upper model, emphasis is placed on demand response strategies to optimize load-side dispatch. This includes encouraging customers to adjust their electricity consumption patterns through time-of-use pricing and effectively managing controllable loads for peak shaving and valley filling. These actions collectively aim to maximize the virtual power plant's overall performance. The upper-tier model then communicates the power output to the lower-tier model. In the lower model, we consider the costs associated with wind, photovoltaic, thermal, and energy storage power generation to optimize power-side scheduling. This approach ensures a comprehensive optimization process, addressing both demand and power generation aspects of the virtual power plant's operations. Compared to existing research, this paper takes into account the impact of various clean energy sources, meticulously categorizes controllable loads, and conducts in-depth modeling and analysis of demand response. Finally, the results of optimal scheduling of the virtual power plant under different scenes are compared and analyzed using examples, to validate the effectiveness of the proposed method.

In view of the challenges of renewable energy output and the uncertainty of electricity prices to the operation of virtual power plants, a multi-stage robust optimization based on the risk-control model of virtual power plant alliances and market bidding strategies are proposed .In view of the wind power output and market price fluctuations under the box-type uncertain set, consider multiple adjustable resources such as electric vehicles and energy storage, establish a single virtual power plant adjustable resource and a multi-stage robust collaborative control model of the wide-area virtual power plant alliance, and enhance The ability to withstand uncertain risks and improve market efficiency. A robust dual dynamic programming algorithm is proposed to solve the proposed model, and an improved lower bound of the hyperplane is proposed to solve the problem that the traditional benders lower bound cannot approximate the saddle value function surface. The simulation results show that the proposed model can provide an important basis for virtual power plant operators' market participation strategies, and promote the promotion and application of virtual power plants in the future power market.

Virtual power plant (VPP) is a carrier that organically combines distributed generation, dispatchable load, and distributed energy storage, and then realizes the integrated regulation of various distributed energy storage (DER) through matched regulation methods and communication technology. Combined with the construction of an emerging power system, this paper expounds in detail on the operation characteristics and functional characteristics of the virtual power plant as well as its participation in the power market and auxiliary service market. Finally, according to the operation requirements of virtual power plant, the operation management platform of the virtual power plant is studied and designed, which includes the functions of virtual power plant integrated monitoring, power load management and prediction, virtual power plant regulation strategy management, virtual power plant operation management, and virtual power plant operation regulation.

In allusion to virtual power plants composed of distributed generators and energy storages, a two-stage robust optimal scheduling model, in which the risk was taken into account, was proposed to deal with the impact of distributed renewable energy uncertainty on the operation of virtual power plants. In the first stage of this model the uncertainty set of the robust model was optimized, meanwhile the risk of renewable energy curtailment and that of loss of load were evaluated. In the second stage, the worst operation scenario was searched within the optimized uncertainty set, and the operation cost of the virtual power plant under this scenario was optimized. Cooperatively solving the two stage models, an appropriate robust model uncertainty set could be determined and a dispatching scheme to cope with the worst operation scenario could be enacted to ensure both economy and security of virtual power plant operation. By means of the linearization of the two kinds of risks, the proposed model could be solved by the state of the art column-and-constraint generation algorithm. Finally, the effectiveness of the proposed model is verified by case study.

The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants. Besides, the game relationship between transaction subjects needs to be further explored. This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game. Firstly, a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant, considering the energy consumption characteristics of users. Secondly, the utility functions of multiple virtual power plants are analyzed, and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process. Finally, the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies. Furthermore, the effect of different parameters on the Nash equilibrium point is explored, and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized. According to the obtained results, compared with the central air conditioning set as constant temperature control strategy, the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs. In addition, the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs. When the service quotation decreases gradually, the P2P transaction between VPPs is more likely to occur.

The load of central air conditioning can be transferred or reduced through reasonable control and has become an effective alternative resource for the adjustable capacity of power grid. In the demand response mode, the central air-conditioning load can be aggregated to form a virtual power plant to participate in the unified grid dispatching operation. In this paper, the virtual energy storage model of the central air-conditioning system is built based on the integrated thermal inertia. The paper also proposed the internal coordination control mechanism based on multiple agents. Furthermore, the regulatory potential of the central air-conditioning system is evaluated from the perspectives of adjustable power and sustainable time according to the virtual energy storage characteristics. The virtual power generation unit is constructed by means of comprehensive coordinated control so as to realize the standardized unified modeling of central air conditioning system. Finally, This paper proposes the strategy of virtual generating unit dispatch based on local autonomous optimization and the operation strategy of virtual power plant participating in grid peak regulation. Through the strategy the distributed optimal control of power generation resources is realized in virtual power plant and the pressure on virtual power plant operators is released. This paper has guiding significance for virtual power plant to participate in the actual dispatching operation of power network.

As a priority industry in China to be included in the carbon trading system, the power industry has become the main body of carbon emission reduction. As a technology and market solution for aggregating distributed resources in the power grid, virtual power plants can further enhance the overall benefits of virtual power plants by aggregating electric vehicle resources to participate in the carbon market. This paper proposes an optimal scheduling method for virtual power plants to aggregate electric vehicles to participate in the carbon market. Electric vehicles are used as controllable loads and energy storage devices to participate in the optimal operation of virtual power plants, and to improve the economic benefits of virtual power plants participating in the electric energy market. The power plant participates in the certified emission reduction market by acting as an agent for electric vehicles, which improves the efficiency of electric vehicles participating in the carbon market. The analysis of an example shows that the advantages of traditional distributed resources and electric vehicles can be effectively complemented by the centralized optimal management of virtual power plants and simultaneous participation in electricity energy market and carbon market transactions, and the overall operation performance of virtual power plants can be improved.

A virtual power plant (VPP) can be defined as the integration of decentralized units into one centralized control system. A VPP consists of generation sources and energy storage units. In this article, based on real measurements, the charging and discharging characteristics of the battery energy storage system (BESS) were determined, which represents a key element of the experimental virtual power plant operating in the power system in Poland. The characteristics were determined using synchronous measurements of the power of charge and discharge of the storage and the state of charge (SoC). The analyzed private network also includes a hydroelectric power plant (HPP) and loads. The article also examines the impact of charging and discharging characteristics of the BESS on its operation, analyzing the behavior of the storage unit for the given operation plans. The last element of the analysis is to control the power flow in the private network. The operation of the VPP for the given scenario of power flow control was examined. The aim of the scenario is to adjust the load of the private network to the level set by the function. The tests of power flow are carried out on the day on which the maximum power demand occurred. The analysis was performed for four cases: a constant value limitation when the HPP is in operation and when it is not, and two limits set by function during normal operation of the HPP. Thus, the article deals not only with the issue of determining the actual characteristics of charging and discharging the storage unit, but also their impact on the operation of the entire VPP.