Virtual Power Plant Model Research Articles

Research on Optimal Operation Model of Virtual Electric Power Plant Considering Net-Zero Carbon Emission

“World Energy Outlook 2021” has mentioned the fact that with the current pledges announced, the 2050 net-zero carbon emission target would not be realized. To further improve energy efficiency, energy integration will be important. Therefore, this paper introduced virtual power plant (VPP) and power to gas (P2G) technology to analyze the improvement of energy integration. Firstly, the structure of VPP connected with P2G is proposed, and the physical output model is constructed. Secondly, combined with carbon emission and economic operation objectives, a multi-objective operation optimization model of VPP considering electrical interconnection is constructed, and the solution idea of the model is put forward. Finally, through the case study, the contribution of P2G, DR and GST is proven. With DR, P2G involved in VPP, the goal of carbon emission reduction can be achieved. In addition, the example also proves that carbon trading has a positive effect on energy efficiency and generation uncertainty improving.

Research on Trading Optimization Model of Virtual Power Plant in Medium- and Long-Term Market

In the medium- and long-term market, the power generation side and the power purchase side ensure to avoid the fluctuation of delivery prices through the medium- and long-term power contract, to avoid some market risks. This paper combines virtual power plants to aggregate distributed renewable energy to participate in market transactions. Firstly, this paper analyzes the two operation modes of power markets and combs the transaction varieties and modes in the medium- and long-term market. Secondly, the common contract power decomposition methods in the medium- and long-term market are analyzed, and the revenue model of virtual power plants is established. Then, combined with the renewable energy quota system and the green certificate trading mechanism, this paper constructs an optimization model of medium- and long-term contract trading of virtual power plants considering renewable energy derivatives. Finally, different renewable energy output scenarios are designed to analyze the benefits of virtual power plants in centralized and decentralized power markets. The example analysis shows the effectiveness of price difference contract for virtual power plants to ensure the renewable power revenue, which provides a certain reference for virtual power plants to participate in the power market.

Design of Virtual Power Plant System Model under Master-Slave Multi-chain

The emergence and development of new technology provides a new way of thinking for solving the problems in the traditional energy industry. Introducing block chain technology into virtual power plant can solve the problems existing in virtual power plant such as. to improve the safety, reliability and operation efficiency of virtual power plant. At present, the research on the application of block chain technology in virtual power plants is mostly based on single-chain structure, which makes the system not extensible. Based on this problem, a virtual power plant model with main and multi-chain structure is presented, which improves the scalability of virtual power plant with block chain technology. The use of block chain technology improves the security and fairness of power transactions. It can allocate power more efficiently to improve energy utilization.

A data-driven scheduling model of virtual power plant using Wasserstein distributionally robust optimization

Distributed energy resources (DER) can be efficiently aggregated by aggregators to sell excessive electricity to spot market in the form of Virtual Power Plant (VPP). The aggregator schedules DER within VPP to participate in day-ahead market for maximizing its profits while keeping the static operating envelope provided by distribution system operator (DSO) in real-time operation. Aggregator, however, needs to make a decision of its offer for biding under the uncertainties of market price and wind power. This paper proposes a two-stage data-driven scheduling model of VPP in day-ahead (DA) and real time (RT) market. In DA market, in order to determine VPP output for biding, a piece-wise affine formulation of VPP profits combing with CVaR for avoiding market price risk is constructed firstly, and then a data-driven distributionally robust model using a Wasserstein ambiguity set is constructed under uncertainties of market price and wind forecast errors. A set of data-driven linearization power constraints are applied in both DA and RT operation when the parameters of distribution network are unknown or inexact. The model then is reformulated equivalently to a mixed 0–1 convex programming problem. The proposed scheduling model is tested on the IEEE 33-bus distribution network showing that under same 1000-sample dataset in training, proposed DRO model has over 85% of reliability while the stochastic optimization has only 69% under the market risk, which means the proposed model has a better out-of-sample performance for uncertainties.

ANN-Based Binary Backtracking Search Algorithm for VPP Optimal Scheduling and Cost-Effective Evaluation

This article reports an artificial neural network (ANN)-based binary backtracking search algorithm (BBSA) for optimal scheduling controller applied on IEEE 14-bus system for controlling microgrids (MGs) formed virtual power plant (VPP) toward sustainable renewable energy sources (RESs) integration. The model of VPP was simulated and validated based on actual parameters and load data reported in Perlis, Malaysia. BBSA optimization algorithm offers the best binary fitness function to find the best cell. It creates the optimum scheduling using the actual data for wind speed, solar radiation, fuel conditions, battery charging/discharging, and specific hour demand. The developed ANN-based BBSA search for the optimal ANN parameters architecture, e.g., (the number of neurons and learning rate) that enhanced the ANN controller to predict the optimal schedules to regulate power-sharing via prioritizing the utilization of RES in place of the national grid purchases. The results of the optimal <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on/off</small> status prediction of the 25 DGs showed that the ANN-BBSA gives a mean absolute error (MAE) of 6.2 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> with a unity correlation coefficient. The results showed a significant reduction in the cost and emission by 41.88% and 40.7%, respectively. Thus, the developed algorithms reduced the energy cost while delivered reliable power toward grid decarbonization.

Optimal Reserve and Energy Scheduling for a Virtual Power Plant Considering Reserve Activation Probability

With the increasing share of variable and limitedly predictable renewable energy in power systems worldwide, ensuring reserve capacity to maintain the balance of supply and demand becomes more important. On the other hand, the development of the virtual power plant model (VPP) allows renewable sources and energy storage to participate in reserve service. This paper addresses the optimal reserve bidding strategy problem of a VPP comprising of renewable energy resources (RESs), energy storage systems (ESSs), and several customers. The VPP participates in balance capacity (BC), day-ahead (DA), and intra-day (ID) markets. The scheduling problem is formulated as a two-stage chance-constrained optimization model taking the uncertainty of RESs production, load consumption, and probability of reserve activation into account. The response of VPP after its reserve capacity is called and generated is also considered to increase the operational flexibility of VPP. The proposed model is implemented on a test VPP system, and the effects of RESs sizing, ESSs sizing, and the probability of reserve activation are analyzed. Results indicate that the proposed model can perform well under real-world conditions.

Aggregated Model of Virtual Power Plants for Transient Frequency and Voltage Stability Analysis

The Virtual Power Plant (VPP) has been proposed to aggregate Distributed Generations (DGs) to act like a single power plant, thus, also has functions on the frequency and voltage support. The previous models of the VPP are static and focus on the energy trading and management. For the system transient response analysis, a dynamic VPP model must be needed. The paper proposes a reduced-order yet accurate aggregated model to represent VPP transients for the stability analysis of power systems. The goal is to provide a model that is adequate for system studies and can serve to the Transmission System Operator (TSO) to evaluate the impact of VPPs on the overall grid. The proposed model can accommodate the transient response of the most relevant controllers included in the distributed generators that compose the VPP. Using a comparison with a real-time detailed Electro-Magnetic Transients (EMT) models of the VPP confirms the validity of the proposed aggregated model. The case studies based on the IEEE 39-bus system verifies the accuracy of the proposed aggregated model on the system stability analysis.

Three-level market optimization model of virtual power plant with carbon capture equipment considering copula–CVaR theory

To achieve carbon neutrality, promoting clean and renewable energy consumption and reducing CO2 emissions have become important measures. Therefore, virtual power plants (VPPs) containing carbon capture devices have become a research focus. This study first builds a VPP model containing carbon capture devices, analyzes the cooperative operation mode of a carbon capture system and power-to-gas, and develops a comprehensive demand response mechanism for the VPP. Second, owing to the uncertainty of the electricity market clearing price and the natural gas market price, a risk dependence model between electricity price and gas price based on the copula–conditional value-at-risk theory is constructed. Then, a VPP three-level market optimization model considering risk dependence and carbon trading mechanisms is proposed, and an improved collaborative evolutionary algorithm that combines the NSGAII and AGE-MOEA algorithms is used to solve multi-objective problems. Finally, an example of a VPP is used to verify the effectiveness of the model.

Robust Optimal Scheduling Model of Virtual Power Plant Combined Heat and Power Considering Multiple Flexible Loads

Robust Optimal Scheduling Model of Virtual Power Plant Combined Heat and Power Considering Multiple Flexible Loads

Optimal Scheduling Model of Virtual Power Plant and Thermal Power Units Participating in Peak Regulation Ancillary Service in Northeast China

In recent years, the rapid development of wind power generation and the low load growth rate have brought challenges to the peak regulation of power grid in Northeast China. In order to solve the peak regulation problem, virtual power plant (VPP) is proposed to participate in the peak regulation auxiliary service market in Northeast china, especially for deep peak regulation. Firstly, two modes of peak regulation in Northeast China are introduced in this paper, which are thermal power unit and VPP. With the definition of the peak regulation capacity for VPP, the Optimal Scheduling Model of VPP participating in peak regulation ancillary service is established. The simulations are carried out in MATLAB. The results show that the economy of peak regulation is significantly improved in peak regulation auxiliary service based on VPP.

Study on Economic Operation and Optimal Dispatching of VPP

Large scale grid-connected wind power will have a great impact on the power grid. In order to ensure the safetyof power grid operation, it is necessary to quickly adjust the load of thermal power plants to stabilize the fluctuation ofwind power output. The economy of thermal power unit is poor in low load operation, and the safety and service life of theunit will be reduced when the load is adjusted frequently. In order to solve the problems, wind and thermal powercomplementarity is proposed, and the wind farm and thermal power plant constitute a virtual power plant (VPP) toparticipate in the grid dispatching. A multi-objective optimal dispatching model of VPP considering economy and rapidity isestablished, which is solved by improving genetic algorithm. The results show that the multi-objective optimal dispatchingmodel can make the economy of VPP the best on the premise of meeting the requirements of grid dispatching time throughthe simulation of differentscenarios. Furthermore, the strategy of selecting the weight coefficient of multi-objectiveoptimization model is given, and the wind power consumption capacity of VPP is analysed.

Research on Two-stage Game Strategy of Virtual Power Plant in Deep Peak Regulation Auxiliary Service Market

There is a large number of combined heat and power units in northern China, and due to the limit of heating demand, the operating mode of setting electricity by heat of combined heat and power units has seriously took over the consumption space of other energy, resulting in severe wind power curtailment and rationing situation in some areas, so this paper studies the deep peak regulation bidding strategy problem considering multiple uncertainties on virtual power plants, and established a two-staged optimization model of virtual power plant to maximize the net revenue, then introduced the Shapely value method with correction coefficient redistribute the peak regulation revenue. The simulation results showed that the two-stage bidding model can not only improve the market competitiveness of the virtual power plant, but also promote the consumption of renewable energy and reduce the market peak regulation service cost. Meanwhile, the improved apportion method can effectively guarantee the enthusiasm of all kinds of units to participate in the deep peak regulation market.

A multi-energy complementary virtual power plant operation optimization model for distributed energy aggregation is considered

In this paper, a stochastic scheduling optimization model of virtual power plant (VPP) is established to maximize the net operating income of VPP considering the loss of uncertain risks. Firstly, the model of conventional scheduling optimization of virtual power plant is presented. Then, the risk conditional value (CVaR) theory is introduced to quantify the operational risk level of VPP, and the constraint conditions containing random variables are converted by the confidence method to establish the risk avoidance scheduling optimization model of VPP. Finally, an improved IIEEE30 node system is used as a simulation system to verify the validity and applicability of the proposed model.

Virtual Power Plant Participates in the Two-Level Decision-Making Optimization of Internal Purchase and Sale of Electricity and External Multi-Market

The power industry’s participation in carbon trading and green certificate trading is an effective market-based approach to solve the negative externalities of power production. In this paper, the Virtual power plant (VPP) is taken as the aggregator to coordinate and optimize the carbon trading and green certificate trading between the power purchasing end and the power selling end, so as to achieve the goal of maximizing the comprehensive benefits of the VPP. Firstly, the operation model of VPP aggregating various types of distributed energy and different users participating in green certificate market and carbon trading market is analyzed; Secondly, a two-level collaborative optimization model of VPP participating in power purchase and sale transaction and green certificate transaction is constructed. On the one hand, the cost of power purchase and green certificate acquisition is minimized by combining various types of power generation resources at the power purchase end. On the other hand, the power purchased is distributed among various types of users at the power sale end, so as to maximize the power sale income and green certificate sales income. On this basis, the VPP as a whole participates in the electric energy market, carbon trading market and green certificate trading market to maximize the comprehensive income. Finally, a VPP is taken as an example to verify the economy and effectiveness of the proposed model in this paper.

Bidding Strategy for Virtual Power Plant Considering the Large-Scale Integrations of Electric Vehicles

In order to alleviate the energy crisis and environmental pollution, electric vehicles (EVs) have been developed on a large-scale throughout the world in the exceptional period. On the other hand, virtual power plant (VPP), an aggregator involved in various renewable energies and diversified loads, is considered as the crucial measure to coordinate with the EVs to participate in the power market. Obviously, the equivalent model of EV in the VPP is difficult to fulfill the need for large-scale development of EVs in the future. Therefore, it is of great significance to establish the detailed charging models of EVs and explore the influences on the operation of the VPP. First, the basic structure and operation feature of VPP considering four different kinds of EVs is introduced briefly. Moreover, in order to describe the bidding processing of VPP, the three stages of power market are introduced from the trading aspects. Then, the charging characteristics of four types of EVs are analyzed in terms of charging time, charging power, and access probability. In addition, the bidding strategy models of VPP at three different stages are built and the improved Artificial Bee Colony algorithm is utilized to solve the optimal bidding strategies of the VPP. Finally, the performances of the bidding model of VPP are validated by the different scenarios that considered the multitype EVs integrations, and the influences of the different scales of EVs on the bidding strategy of VPP are also analyzed.

Optimal dispatching of electric-thermal interconnected virtual power plant considering market trading mechanism

With the development of energy internet technology, electric-thermal interconnected virtual power plant, a new integrated demand response entity, tends to become an effective solution to solve the high reliability and efficient utilization of distributed energy systems. Based on this, a new optimal dispatching method for electric-thermal interconnected virtual power plants considering market trading mechanism is established, which can effectively solve the operation and market transaction problems of complex energy systems including electricity, thermal, natural gas and other energy sources. In this paper, the structure and market trading mechanism of electric-thermal coupled virtual power plants are firstly studied. Secondly, a two-stage optimization model of virtual power plant is constructed. In the first stage, a multi-bid day-ahead market bidding model considering both economic and social benefits is constructed, which is aiming at formulating the strategy and bidding value of virtual power plant participating in energy market dispatching. In the second stage, the intra-day scheduling model is constructed, which takes the minimum penalty cost including abandoned wind penalty cost and bidding penalty cost as the optimization objective, and the output of virtual power plant is adjusted to ensure the operation efficiency of the system. Finally, an electric-thermal coupled virtual power plant in Tianjin, China, is taken as an example for analysis. The results show that the two-stage optimization model presented in this paper can significantly improve the volatility of renewable energy output and the economy of the system operation under the conditions of ensuring the comprehensive benefits of virtual power plant and participating in energy market bidding efficiently.

Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model

The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs.

Two-Stage Congestion Management Considering Virtual Power Plant With Cascade Hydro-Photovoltaic-Pumped Storage Hybrid Generation

With the widespread access of flexible loads and renewable energy, the power flow often exceeds the limit of line, resulting in the blockage of distribution network. Virtual power plant (VPP) can not only integrate multiple forms of energy, but also improve the penetration proportion of renewable energy sources. In view of the excellent ability of VPP in power market, a two-stage congestion management (CM) model involving virtual power plant (VPP) with cascade hydro-photovoltaic-pumped storage hybrid generators is established in this paper. The first stage is congestion management of ISO (Independent System Operator), which aims at minimizing congestion cost and is solved by genetic algorithm. The second stage is power correction model of virtual power plant, which is a multi-objective problem considering water and light curtailment, navigation and ecological needs. An improved hybrid non-dominated sorting genetic algorithm-II (HNSGA-II) is proposed to solve the two-stage model. Case studies show that the participation of virtual power plants in congestion management has certain economic value to power system. The joint dispatch of cascade hydro-photovoltaic-pumped storage hybrid generation in the virtual power plant can make flexible decisions according to the needs of energy saving, navigation and ecology. The improved HNSGA-II algorithm is superior to the traditional NSGA-II algorithm in convergence, uniformity and extensity.

Optimal Cost Management of Distributed Generation Units and Microgrids for Virtual Power Plant Scheduling

Virtual power plant (VPP) interconnects distributed generation (DG) units, microgrids, and energy storage systems (ESSs) of an electrical power system. This article presents a linear programming cost minimization model of VPP for the design and commitments of DG, ESS, and microgrid. Using a set of renewable energy resources, the proposed model creates a reliable, cost-effective, and environmentally friendly distribution system. Accurately, it illustrates the schedule of the VPP to operate autonomously. The proposed model is applied to a set of United States commercial load profiles to determine the investment benefit of implementing DGs in the power system. Analysis of results concerning variation in energy price illustrates feasible solutions. VPP decision-makers can select the best reasonable solution based on their specific project budget for feature electricity generation. Moreover, results show the need for the proposed method in VPP decision making.

A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Economic Aspects

This paper analyzes the technical and economic possibilities of integrating distributed energy resources (DERs) and energy-storage systems (ESSs) into a virtual power plant (VPP) and operating them as a single power plant. The purpose of the study is to assess the economic efficiency of the VPP model, which is influenced by several factors such as energy price and energy production. Ten scenarios for the VPP were prepared on the basis of the installed capacities of a hydropower plant (HPP), rooftop solar photovoltaic (PV), and energy-storage system (ESS), as well as weather conditions, in Poland. On the basis of technical conditions, it was assumed that the maximum power capacity of the ESS equaled 1.5 MW. The economic efficiency analysis presented in this paper demonstrated that, in seven years, the VPP will achieve a positive value of the net present value (NPV) for a scenario with 0.5 MW battery storage and rainy summers. Furthermore, sensitivity analysis was conducted on price factors and DER production volume. The price variable had a major impact on the NPV value for all scenarios. The scenario with a 0.5 MW battery and typical summers was highly sensitive to all factors, and its sensitivity decreased as the ESS capacity grew from 0.5 to 1.5 MW.