A Case Study of a Virtual Power Plant (VPP) as a Data Acquisition Tool for PV Energy Forecasting

The electricity supply chain is changing, due to increasing awareness for sustainability and an improved energy efficiency. The traditional infrastructure where demand is supplied by centralized generation is subject to a transition towards a Smart Grid. In this Smart Grid, sustainable generation from renewable sources is accompanied by controllable distributed generation, distributed storage and demand side load management for intelligent electricity consumption. The transmission and distribution grid have to deal with increasing fluctuations in demand and supply. Since realtime balance between demand and supply is crucial in the electricity network, this increasing variability is undesirable. Monitoring and controlling/managing this infrastructure increasingly depends on the ability to control distributed appliances for generation, consumption and storage. In the development of control methodologies, mathematical support, which consists of predicting demand, solving planning problems and controlling the Smart Grid in realtime, is of importance. In this thesis we study planning problems which are related to the Unit Commitment Problem: for a set of generators it has to be decided when and how much electricity to produce to match a certain demand over a time horizon. The planning problems that we formulate are part of a control methodology for Smart Grids, called TRIANA, that is developed at the University of Twente.

The aim of the work is the integration of combined heat and power (CHP) micro-units into the low voltage network under technical and economical point of view. The whole power and gas consumption of a new residential district are measured and recorded in fifteen minutes intervals during one year. Based on these power and gas load curves the basis of a virtual power plant with CHP micro-units is analysed. With a simulation tool the operation of a CHP micro-unit is simulated. The results show the behaviour of single units in different types of houses and of all units in a virtual power plant. Furthermore they show how the operation of a CHP micro-unit must be optimized for the operation in a virtual power plant. On the basis of this technical parameters a business model for an virtual control power plant is presented and calculated.

To achieve the national carbon-peak and carbon-neutral strategic development goals, it is necessary to build power systems dominated by renewable and sustainable energy. The future power system with a high proportion of renewable and sustainable energy is required to have large-scale, low-cost, flexible, and adjustable resources. To this end, this article aggregates user-side distributed energy storage and electric vehicles into a virtual power plant, considering the uncertainty of wind power fluctuations and the uncertainty of electric vehicle charging and discharging to establish a day-ahead and intra-day peak regulation model for combined peak regulation of virtual and thermal power plants. The bounding algorithm seeks the optimal strategy for the two-stage model of joint peak regulation and obtains the day-ahead and intra-day two-stage optimal peak regulation strategy. The simulation example shows that the virtual power plant and its day-ahead and intra-day optimal peak regulation strategy can reduce the peak regulation cost of the power system, as compared with the deep peak regulation of thermal power plants with a special supporting energy storage power station. This work provides a global perspective for virtual power plants to participate in the formulation of power system peak regulation rules.

This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.

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

The article describes the features of the effect of distributed generation sources to the power grid, as well as features for virtual power plants on the basis of sources of distributed generation. Characterized intelligent net-works, identify key technological tools and the basic approaches to constructing them. The functional character-istics of networks, the development of which is stipulated by the concept of Smart Grid. The role of active user in the system. The characteristics of virtual power plants, their structure and working principles. Described the benefits of virtual power plants for power suppliers to network organizations, as well as for consumers. The positive effect of a virtual power plant to the consumer. The case of an emergency at one of the sources of electricity Virtual Power Plant. Analyzed the effect of distributed generation sources to distribution networks work in normal mode, and the changes that result from such a connection. Namely: the impact of distributed generation on voltage regulation, the impact of distributed generation sources for losses, the impact of distrib-uted generation sources to the higher harmonics. The reasons of the higher harmonics in power systems and generators of higher harmonics in the sources of distributed generation. The factors to consider when choosing the main equipment for virtual power plants, and the factors that determine the choice of the nominal voltage of virtual power plant. The peculiarities of the choice of the generator. The basic principles of coordination of the power equipment of virtual power plants. Characterized modular design of virtual power plants and shows its advantages. Describes the principle of parallel operation of generators virtual power plant with other power plants and distribution network, and cases in which such work is effective. The change of the electric network mode after connecting a virtual power plant. The main issues of integration of distributed energy generation to the grid. The features of circuits connected objects of distributed generation to the network, and the require-ments for interconnection of distributed generation components.

Benefits and cost implications from integrating small flexible nuclear reactors with off-shore wind farms in a virtual power plant

Energy plants based on renewable energy sources can be combined according to the type of energy source (hydro, solar, wind with or without hydro storage). Currently, not enough attention has been paid to assessing the economic efficiency of combined power plants incorporating a hydraulic accumulator. When designing combined power plants, it becomes necessary to choose the composition of such plants and evaluate their economic efficiency. The purpose of this study is to justify the possibility of applying the present value method to select the composition of combined heat and power plants and assess their economic efficiency. The research used the following methods: review, synthesis of existing literature on the subject, system analysis, collection of data on different types of power plants, and calculation of their economic efficiency. A computer program has been compiled with Turbo Pascal 7.0 for the calculations. The calculations have shown that a cost-effective option is a combined power plant based on a wind power plant and a hydropower plant with a hydro accumulator.

Due to the existence of wind turbines in virtual power plants (VPPs), to eliminate the fluctuation characteristics of the wind turbine in the VPP and quantify the impact of the capacity of the energy storage system (ESS) on VPPs’ profitability, a multi-objective optimal schedule model, including the operating cost of the VPP, operation cost of the ESS and the electricity cost between the VPP and the grid, was proposed, and was solved by a quantum-inspired evolutionary algorithm; the output of conventional distribution generation and the state of charge were analysed on two different load days; the impact of different sub-object weights on the ESS capacity and the ESS cost and VPP profit under different rated capacities were also investigated. The results showed that the wind turbines' output error can be eliminated by the ESS on peak load day and valley load day; different weights of the VPP operating cost and the trading power cost would result in different capacities of the ESS; and the VPP's profit would rise with the increase of the ESS capacity when the capacity of the ESS was less than 8 MW.

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Economic assessment of multi-operator virtual power plants in electricity market: A game theory-based approach

Virtual power plant (VPP) can effectively control multiple kinds of distributed energy resources (DERs) in different geographical locations. Given that VPPs may belong to different owners and beneficiaries, this study proposes a non-cooperative game-based coordination control (NCGCC) method for the VPPs. In the method, the NCGCC is proposed to coordinate adjustable photovoltaic (PV) power resources, energy storage and other DERs to reduce the total generation cost within the VPP. The cost models of different beneficiaries are established to consider the economic benefits of each VPP, and the NCGCC among the VPPs is used to solve the competition problem among VPPs. Each VPP bids to undertake power generation tasks in order to minimize the cost. Finally, the simulation results of typical multi-VPP system under various operating conditions are compared and analyzed to verify the effectiveness and applicability of the proposed method.

A scheduling framework for VPP considering multiple uncertainties and flexible resources

This study intends to optimize the trading decision-making strategy of the new electricity market with virtual power plants and improve the transmission efficiency of electricity resources. The current problems in China’s power market are analyzed from the perspective of virtual power plants, highlighting the necessity of reforming the power industry. The generation scheduling strategy is optimized in light of the market transaction decision based on the elemental power contract to enhance the effective transfer of power resources in virtual power plants. Ultimately, value distribution is balanced through virtual power plants to maximize the economic benefits. After 4 hours of simulation, the experimental data shows that 75 MWh of electricity is generated by the thermal power system, 100 MWh by the wind power system, and 200 MWh by the dispatchable load system. Comparatively, the new electricity market transaction model based on the virtual power plant has an actual generation capacity of 250MWh. In addition, the daily load power of the models of thermal power generation, wind power generation, and virtual power plant reported here are compared and analyzed. For a 4-hour simulation run, the thermal power generation system can provide 600 MW of load power, the wind power generation system can provide 730 MW of load power, and the virtual power plant-based power generation system can provide up to 1200 MW of load power. Therefore, the power generation performance of the model reported here is better than other power models. This study can potentially encourage a revised transaction model for the power industry market.

As an important manifestation of the current development and transformation of the world’s power and energy industries, the virtual power plant is an important foundation for optimizing the layout of energy resources. However, since there are many open channels in the virtual power plant, adversaries can implement eavesdropping, replay, impersonation, forgery, and other attacks to access the virtual power plant, and even publish false data in the virtual power plant to disrupt the operation of the virtual power plant. In addition, it is easy for an adversary to deduce key information such as the layout of virtual power plant equipment through the identity of the device. In this context, to ensure the security and privacy of devices when accessing the platform, in this paper, we propose an efficient authentication protocol based on the elliptic curve cryptography and zero-knowledge proof, which requires only two information exchanges. Security analysis shows that the proposed protocol can meet security features such as mutual authentication, key agreement, perfect forward secrecy, and device anonymity. Performance analysis indicates that the proposed protocol achieves a reasonable balance between computational and signaling overhead, and it is more suitable for achieving efficient device authentication and privacy protection in virtual power plants.