Virtual Plant Model Research Articles

Rolling optimization method of virtual power plant demand response based on Bayesian Stackelberg game

To optimize the interaction effect between internal units and demand response of virtual power plants and enhance their transaction profit, a study on the the rolling optimization method of demand response for virtual power plants based on Bayesian Stackelberg game is conducted. Following the construction of a virtual power plant model and analysis of its operation strategy and process content, this method employs a power demand forecasting approach based on multidimensional fusion and Bayesian probability update to forecast the demand-side power requirements within the jurisdiction of the virtual power plant. Utilizing the forecast results of dynamic electricity demand, a demand response elastic matrix for virtual power plant is constructed through a rolling optimization model based on Stackelberg game. The two optimization objective functions, maximizing the supply-side income and minimizing the demand-side electricity purchase cost of virtual power plant, are transformed into maximizing the profit of power transaction for the virtual power plant. This is iteratively solved using the whale algorithm to determine the optimal power generation distribution scheme for each unit on both the supply side and demand sides. Upon testing, this method demonstrates not only the capability for peak shaving and valley filling but also improves the operating profit of the virtual power plant and optimizes user satisfaction, resulting in a relatively high comprehensive benefit index.

Model of virtual power plant with energy storage and adjustable load participating in energy market and auxiliary service

Model of virtual power plant with energy storage and adjustable load participating in energy market and auxiliary service

Joint Optimization of Multienergy Virtual Power Plant Configuration and Operation Considering Electric Vehicle Access

The problems of energy shortage and environmental pollution can no longer be ignored. How to make the best of energy and improve energy efficiency has always been a concern of researchers. The rapid development of electric vehicles (EVs) has made them an energy load that cannot be ignored. On this basis, an optimal configuration model of a multienergy virtual power plant (MEVPP) considering EV access is constructed to meet the multiple energy needs. To better consider EV users’ willingness to respond, this paper combines price demand response (PDR) with incentive demand response (IDR), establishes a fuzzy response model for EV charging and discharging under the joint response strategy (JRS), and analyzes the influence of user responsiveness and large‐scale EV access on MEVPP planning and operation under different incentive levels. Meanwhile, to realize the low carbon, a stepped carbon trading mechanism (SCTM) is introduced. Based on the gazelle algorithm and mixed integer linear programming (MILP), the capacity and output of the system energy equipment are jointly optimized, and the running curve of MEVPP in a typical quarter is analyzed. The example analysis shows that the joint response strategy proposed reduces the operating cost by 7.1%, and the introduction of SCTM reduces the carbon emission by 13.7%, realizing the low‐carbon and economic running of MEVPP.

PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation

PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation

Hybrid solar and hydrogen energy system 0-D model for off-grid sustainable power system: A case in Italy

Off-grid solar systems are one of the most promising solutions for achieving complete grid independence. However, the storage of large amounts of energy produced in the summer through solar panels becomes crucial to reach this goal and hydrogen, as a zero-CO2 energy carrier, could play a pivotal role. This paper presents a case study on the integration and simulation of solar energy and hydrogen technologies in an off-grid energy plant for a teaching buildings complex in Italy. A 0-D virtual energy plant model has been developed aimed at estimating the net energy production and hydrogen consumption/production rates using different inputs of irradiance (monthly average, daily) and energy demand (constant and variable daily consumption levels) in the buildings. The outcome of the analysis identifies the most convenient configuration of the plant in terms of sizing and device interactions for achieving complete grid independence, and the impact of different inputs on the plant performance.

Virtual Power Plant Response Strategy Considering Dynamic Aggregation of Different Flexible Resources

Abstract Aggregate and adjust distributed resources, the virtual power plant realizes the collaborative optimization of different forms of electric energy, which greatly improves the flexibility of the power system. Considering the flexibility of distributed resources, this paper designs the active power and frequency regulation modes of virtual power plants, and proposes a response strategy for dynamic resource aggregation of virtual power plants. Firstly, the distributed energy resources of virtual power plants are classified, and their dynamic characteristics are fully considered for dynamic aggregation, and the active power and based on these characteristics, a frequency regulation model is established. Secondly, according to the real-time operation status of sampled distributed resources, a response prediction model of virtual power plant based on dynamic resource aggregation was proposed. Finally, taking the 35kV substation workbench as an example, the response effect of the virtual power plant at different times of a typical day is verified.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

Solar power forecasting model as a renewable generation source on virtual power plants

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.

Internal revenue sharing methodology for virtual power plant clusters considering carbon incentive and penalty mechanisms

With the rapid development of renewable energy and the urgent need for global carbon emission reduction, virtual power plants have become a high-profile energy management model that can integrate multiple energy resources. How to effectively integrate renewable energy to reduce carbon emissions, how to optimize the use of different energy resources, and how to fairly distribute economic benefits within virtual power plant clusters while encouraging the reduction of carbon emissions are issues that need to be addressed in research. The study first established a virtual power plant model and conducted in-depth optimization for its economic and environmental indicators. Subsequently, the study constructed a game model within the virtual power plant cluster, aiming to solve the problem of income distribution in this diversified energy system. The research results found that commercial users have the highest carbon emissions, followed by industrial users, while residential users have the lowest carbon emissions. In terms of optimized user electricity consumption behavior, the peak-to-valley difference rate of industrial users has been reduced by 17%, and the daily load rate has increased by 6%; the peak-to-valley difference rate of commercial users has been reduced by 12%, and the daily load rate has increased by 6%; The peak-to-trough difference rate for residential users decreased by 8%, and the daily load rate increased by 4%. In addition, the research also proposes a method of internal revenue distribution of virtual power plant clusters based on a carbon reward and punishment mechanism, which provides a new way for the synergy effects and economic benefit distribution of virtual power plants. Research is of positive significance in solving pressing issues in the field of energy management and provides strong support for the development of future sustainable energy systems.

Optimal scheduling of electricity-hydrogen coupling virtual power plant considering hydrogen load response

With the rapid development of hydrogen production by water electrolysis, the coupling between the electricity-hydrogen system has become closer, providing an effective way to consume surplus new energy generation. As a form of centralized management of distributed energy resources, virtual power plants can aggregate the integrated energy production and consumption segments in a certain region and participate in electricity market transactions as a single entity to enhance overall revenue. Based on this, this paper proposes an optimal scheduling model of an electricity-hydrogen coupling virtual power plant (EHC-VPP) considering hydrogen load response, relying on hydrogen to ammonia as a flexibly adjustable load-side resource in the EHC-VPP to enable the VPP to participate in the day-ahead energy market to maximize benefits. In addition, this paper also considers the impact of the carbon emission penalty to practice the green development concept of energy saving and emission reduction. To validate the economy of the proposed optimization scheduling method in this paper, the optimization scheduling results under three different operation scenarios are compared and analyzed. The results show that considering the hydrogen load response and fully exploiting the flexibility resources of the EHC-VPP can further reduce the system operating cost and improve the overall operating efficiency.

Control strategy and research on energy storage unit participation in power system frequency regulation based on VSG technology

When large-scale photovoltaic power plants are integrated into the grid, it will cause the grid-connected capacity to gradually expand, causing greater harm to the safe and stable operation of the power system, which will affect the FM characteristics of the power system. This requires the PV power plant to actively participate in power system frequency control. Through the PV virtual synchronous generator frequency control technology, coupled with the virtual synchronous PV power plant modeling, the PV new energy units can have the same frequency control characteristics as synchronous generator sets. In the virtual synchronous technology, due to the existence of damping windings, electromagnetic damping will be generated, and it is also due to virtual damping that the virtual synchronous technology has the ability to damp power oscillations. It is also because of the excellent virtual inertia and damping characteristics of the virtual synchronous generator in the grid that this FM technology helps to regulate the frequency fluctuations of the new energy grid, meet the requirements of the power system to maintain stable operation, and then realize the large-scale PV grid connection.

Development of a PHIL Real-Time Simulation Testbed for Optimization of Hybrid Power Plant Generation

Future electrical grids will likely include a diverse group of power generation plants. With the growing list of new generation options comes many opportunities for more optimal energy production management and effective resource utilization. Microgrids are an ideal environment for research, development, and validation of those energy optimization strategies. This paper establishes a method for integrating a virtual thermal power plant model with a physical microgrid in a power hardware in the loop testbed. One advantage of these testbeds is their variety of configuration options. In this paper, the combined effects of photovoltaic and concentrating solar power generation are investigated. A conventional thermal power plant with components sized for CSP applications is modeled in the Simscape environment. Actual solar panels provide the photovoltaic element of this experiment. How the plant model and physical hardware interact is described in detail. A PV startup and shutdown event is simulated in real-time. The model responses are shown to be successfully and correctly coupled to electrical power flow in the testbed.

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

Two-stage dispatch optimal model of virtual power plant considering pumped storage

When virtual power plant (VPP) participates in power grid dispatch, it not only faces the problem of market bidding but also has the security and stability problems of voltage, frequency, power flow, and so on. The optimal dispatch strategy of VPP, which participates in a hybrid market, is proposed. The two-stage optimal model is established. An economic dispatch optimal model based on robust control and stochastic linear programming is established to maximize the profit of VPP. Aiming to minimize the difference between the output power of each distribution energy resource and the optimal results of economic dispatch, a safety dispatch optimal model based on a particle swarm optimization algorithm is established. The simulation results with the measured data of the Ningxia power grid show that the two-stage optimal dispatch model is effective.

Offering strategy of a price-maker virtual power plant in the day-ahead market

With the rapid increase of renewable energy sources (RESs), the virtual power plant model (VPP) has been developed to integrate RESs, energy storage systems (ESSs), and local customers to overcome the RESs’ disadvantages. When the VPP’s capacity is large enough, it can participate in the electricity market as a price-maker instead of a price-taker to obtain a higher profit. This study proposes a bi-level optimization model to determine the optimal trading strategies of a price-maker VPP in the day-ahead (DA) market. The operation schedule of the components in the VPP is also optimized to achieve the highest profit for the VPP. In the bi-level optimization problem, the upper-level model is maximizing the VPP’s profit while the lower-level model is the DA market-clearing problem. The bi-level optimization problem is formulated as a Mathematical Problem with Equilibrium Constraints (MPEC), reformulated to a Mixed Integer Linear Problem (MILP), then solved by GAMS and CPLEX. This study applies the bi-level optimization model to a test VPP system, including wind plants (WP), solar plants (PV), biogas energy plants (BG), ESSs, and several customers. The maximum power outputs of WP and PV are 100MW and 90MW, respectively. The total installed capacity of BG is 70MW, while the ESS’ rated capacity is 100MWh. The local customers have the highest total consumption of 100MW. In addition to the VPP, four GENCOs and three retailers participate in the DA market. The results show that the market-clearing price varies depending on the participants’ production/consumption quantity and offering/bidding price. However, based on the optimization model, the VPP can take full advantage of WP and PV available power output, choose the right time to operate BG, then obtain the highest profit. The results also show that with the ESS’ rated capacity of 100MWh, the ESS’ rated discharging/charging power increased from 10MW to 50MW will increase VPP’s profit from 45987$ to 49464$. The obtained results show that the proposed model has practical significance

Development of Virtual Plant Model for Design Rationalization of Fast Reactors by Multi-Level Simulation System

Development of Virtual Plant Model for Design Rationalization of Fast Reactors by Multi-Level Simulation System

Optimal Scheduling of Virtual Power Plant Based on Latin Hypercube Sampling and Improved CLARA Clustering Algorithm

In the context of the “Carbon peak, Carbon neutral” target, the introduction of carbon trading and the connection of new energy generation such as wind power and photovoltaics to the power grid have become important means to achieve a reduction to low carbon emissions. To this end, a virtual optimization model is established to take into account both low-carbon and economic aspects. Firstly, based on the basic concept of a virtual power plant, a virtual power plant model containing wind power, photovoltaic power, a gas turbine, and energy storage is established. Then, considering the uncertainty factors of wind power and PV power generation, Latin hypercube sampling (LHS) is used to simulate wind power and PV output scenarios, combined with the improved CLARA clustering algorithm to reduce the scenarios to form a classical scenario set to reduce the influence of wind power and PV output volatility. Finally, a carbon-trading mechanism and time-sharing tariff are introduced, and the model is solved with the objective function of maximizing the net benefit and minimizing the carbon emission of the Virtual Power Plant. Using arithmetic examples for verification, the results show that the introduction of carbon-trading mechanism can improve the net benefits of the Virtual Power Plant while promoting energy saving and emission reduction.

Optimal Scheduling Research for Day Ahead Market Transaction of Virtual Power Plant Considering Uncertainty and CVaR

Considering the uncertainty of renewable energy generation and the market transaction risk in the day ahead market of virtual power plants, an optimal scheduling model of virtual power plant in the day ahead market was presented. Firstly, this study modeled and analyzed the uncertainty of wind and photovoltaic power generation fluctuations in the virtual power plant. In addition, based on the conditional value at risk theory and the day ahead market clearing rules, the optimal scheduling model of virtual power plants in the day ahead market was established aiming at minimizing the implementation deviation and maximizing the operating income. Finally, the transaction data of a typical region was selected for case study to verify the effectiveness and the feasibility of the optimal scheduling strategy of virtual power plant in day ahead market transaction.

Designing of multi-objective optimal virtual power plant model for reliability enhancement in radial network: a case study of Indian power sector

One of the major driving factors in the shifting of the present grid paradigm to an active grid network is the reliability and resiliency of the utility network. With hefty investment in the distribution network protection and maintenance, the reliability of the feeders is considerably enhanced; however, large numbers of outages are still occurring every year which caused major production loss to the manufacturing sector. In this paper, the role of the solar grid-based Virtual Power Plant (VPP) is evaluated in the state power utility for the reliability enhancement and cost minimization using a multi-objective model based on MILP optimization. A 90 bus industrial feeder having automatic reclosers, DER, and DSM is selected on which the MCS method is utilized for computing reliability indices using the utility reliability parameters. The value of reliability indices such as EENS is declined by 68% by utilizing the VPP scenario. These values of this reliability index are fed into the multi-objective model for cost minimization. After running the optimization, the results reveal that the operational and the annual energy cost are reduced by 61% and 55% respectively which advocates the VPP implementation in the utility network. Both modes of the Virtual Power Plant such as grid-connected and autonomous mode have been discussed in detail. Lastly, the results of the developed model with MILP are compared with the proprietary derivative algorithm, and it is found that the proposed MILP is more cost-effective. The overall results advocate the VPP implementation in the utility grid as the economical advantage is provided to both utility and the consumers in terms of reduction in EENS and energy charges respectively.