Day-Ahead Self-Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets Under Uncertainty

The virtual power plant (VPP) has been recognized as an effective way to facilitate penetration of renewable and distributed energy resources in electricity markets. This paper introduces an adaptive curtailment strategy for a VPP comprising a wind power plant and uncertain demand, and explores the economic advantage of adaptively adjusting wind power curtailment. A two-stage stochastic model is proposed to deal with the uncertainties in wind power generation (WPG), load demand and market prices. In the model, the bid decision is made in the face of uncertainties in the first stage, while the control (curtailment) decision is made based on realized uncertain parameters in the second stage. This paper provides the closed-form optimal curtailment decision and characterizes the optimal bid decision. An efficient binary search algorithm is developed for optimizing the bid decision. By using a distribution-free approach, we show that as the prediction accuracy of WPG improves, the optimal bid decision converges toward the expected minimal power exchange, leading to a decrease in expected operational cost with diminishing marginal return. Numerical experiments based on real-world data demonstrate that compared with the existing greedy strategy and coordinated strategy, the proposed model can decrease the expected operational cost up to 16.9% and 11.0%, respectively.

Stochastic power management strategy for optimal day-ahead scheduling of wind-HESS considering wind power generation and market price uncertainties

This paper considers the self-scheduling problem of a virtual power plant trading in both energy and reserve electricity markets. The virtual power plant comprises conventional generation, wind power generation, and a flexible demand that participate in those markets as a single entity in order to optimize the use of energy resources. As a distinctive feature, the proposed model explicitly accounts for the uncertainty associated with the virtual power plant being called upon by the system operator to deploy reserves. This uncertainty and the uncertainty in available wind power generation levels are modeled using confidence bounds, while uncertain market prices are modeled using scenarios. Therefore, the proposed model is formulated as a stochastic adaptive robust optimization problem, which is solved using an effective column-and-constraint generation algorithm involving the iterative solution of a subproblem and a master problem. Results from a case study are provided to illustrate the performance of the proposed approach.

On optimal participation in the electricity markets of wind power plants with battery energy storage systems

A data-driven method to characterize turbulence-caused uncertainty in wind power generation

In the context of the double carbon policy, clean energy is bound to see further development, but the uncertainty of wind and photovoltaic power generation poses many problems for the operation of the grid. Virtual power plant (VPP) technology is an important solution to this problem. As China's electricity market reform continues to progress, it is important to study how virtual power plants can make more profit in the market environment and how to make a reasonable profit distribution for their survival and development. In this paper, the physical and market behaviours of various types of power generation equipment are firstly modelled. Based on this, a bidding model for VPP participation in the day-ahead market is designed with the objective of maximization the overall profit of VPP, and finally the Shapley value method is applied to allocate the profit of VPP. The results of the algorithm show that the operation mechanism of the VPP can take advantage of the different generation methods and smooth out the fluctuations in the output of wind power and PV. In addition, the profitability of each power producer has increased to a certain extent after participating in the VPP, which is conducive to attracting more power producers to participate in the VPP and further improving the profitability of the VPP.

In recent years, the penetration rate of new energy has continued to increase, and the requirements for flexibility of the power system have continued to increase. Virtual power plants (VPPs) have been proposed as a solution. On the one hand, the VPP can assist in stabilizing the fluctuation of the power system by coordinating the aggregated resources, and on the other hand, it can participate in the power market as a whole to obtain economic benefits. How to balance the above two functions is an urgent issue for VPP operators. On the one hand, the wind and photovoltaic power generation aggregated by VPPs has greater uncertainty, which brings difficulties to the scheduling of VPPs. On the other hand, the current mode of VPPs participating in the market is not mature enough, and it is difficult to introduce a large number of VPPs to participate in the power market. Therefore, this paper takes the VPP that includes multiple distributed energy sources such as gas turbine power generation, wind power, photovoltaic power generation, energy storage facilities, and demand response resources as the research object. Based on the uncertainty of wind power and photovoltaic power, this paper analyzes the operating characteristics of internal resources of the VPP, establish a economic operating model, and verify the effectiveness of the model through an example, so that to provide a reference for the practical application of VPPs.

Risk-averse probabilistic framework for scheduling of virtual power plants considering demand response and uncertainties

We consider an energy management system that controls a cluster of price-responsive demands. Besides these demands, it also manages a wind-power plant and an energy storage facility. Demands, wind-power plant, and energy storage facility are interconnected within a small size electric energy system equipped with smart grid technology and constitute a virtual power plant that can strategically buy and sell energy in both the day-ahead and the real-time markets. To this end, we propose a two-stage procedure based on robust optimization. In the first stage, the bidding strategy in the day-ahead market is decided. In the second stage, and once the actual scheduling in the day-ahead market is known, we decide the bidding strategy in the real-time market for each hour of the day. We consider that the virtual power plant behaves as a price taker in these markets. Robust optimization is used to deal with uncertainties in wind-power production and market prices, which are represented through confidence bounds. Results of a realistic case study are provided to show the applicability of the proposed approach.

In this paper, the energy management problem of a virtual power plant (VPP) in energy markets considering contingency conditions is formulated as a stochastic robust optimization model. A number of price-responsive demands, renewable power plants (RPPs), conventional power plants (CPPs), energy storage systems (ESSs) are integrated into a VPP interconnected through an electric network. Smart grid technology enables the energy management system (EMS) to communicate with day-ahead (DA) and real-time (RT) markets, as well as with VPP components. The uncertainties associated with the production of renewable resources, the prices in the DA and RT markets, as well as the availability of the components of the VPP are considered. The uncertainty in the availability of the components in the VPP and the market prices is modeled through scenarios, whereas the variability in renewable production is presented through prediction intervals. Furthermore, the conditional value at risk (CVaR) is incorporated into the model to analyze the impact of risk-aversion on energy management decisions. The results verify the applicability of the decisions under contingency conditions.

Offering strategy of a price-maker virtual power plant in energy and reserve markets

The idea of using wind power to charge electric vehicles (EVs) has attracted more and more attention nowadays due to the potential in significantly reducing air pollution. However, this problem is challenging on account of the uncertainty in the wind power generation and the charging demand from the EVs. Simulation-based policy improvement (SBPI) has been an important method for decision-making in stochastic dynamic programming and, in particular, for charging decisions of EVs in microgrids. However, the problem of allocating the limited computing budget for the best decision-making in online applications is less discussed. We consider this important problem in this work and make the following three major contributions. First, we show that the significant uncertainty in wind power generation forecasting could make the policy that is the outcome of an SBPI worse than the base policy. Second, we apply two existing methods to address this issue, namely, the optimal computing budget allocation (OCBA) for maximizing the probability of correct selection (OCBA_PCS) and the OCBA for minimizing the expected opportunity cost (OCBA_EOC). The asymptotic optimality is briefly reviewed. Third, we numerically compare the performance of OCBA_PCS and OCBA_EOC with the equal allocation (EA), a principle-based method, and a stochastic scenario-based method on small-scale and large-scale experiments. This work sheds light on the EV charging decision in general. Note to Practitioners-Together with the growing adoption of EVs in modern societies, there goes the challenge of how to satisfy the charging demand. Given the high uncertainty both in the wind power generation and in the charging demand, it is important to make decisions online using up-to-date estimation on the renewable power generation and the charging demand. Simulation-based policy improvement (SBPI) is shown both theoretically and practically to be useful to improve a given base policy in various applications, including this EV charging problem. However, the high uncertainty in forecasting could sometimes make the output of SBPI worse than that of the base policy. In this work, we first use numerical experiments to demonstrate the risk for such scenarios. Then, we propose to use two computing budget allocation procedures to address this issue. The asymptotic optimality of both algorithms is briefly reviewed. We demonstrate their performance on numerical experiments when there are only several EVs and when there are 100 EVs.

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

Combining P2G and virtual power plant (Virtual power plant, VPP), this paper proposes a new concept of electrical interconnection virtual power plant (Power-to-gas-based VPP, GVPP). In addition, this paper proposes a GVPP low-carbon economic dispatch optimization model considering carbon emission rights trading. Furthermore, in view of the strong uncertainty of wind power and PV power generation in GVPP, the information gap decision theory is used to measure the uncertainty tolerance threshold of decision makers under different expected target deviations. In addition, a GVPP near-zero carbon random scheduling optimization model is established under the conventional and worst-case scenarios. In order to verify the feasibility and effectiveness of the proposed model, a 9-node energy hub was selected as the simulation system. The results show that: (1) GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. Moreover, it can use the two-way conversion of gas and electricity energy to form an electricity-gas-electricity cycle, thereby improving the system’s clean energy absorption capacity, reducing its own carbon emissions and the volatility of net output. (2) The IGDT method can be used to describe the impact of wind and wind uncertainty in GVPP. Decision makers can obtain the maximum tolerance for wind and wind uncertainty by setting a reasonable expected target deviation coefficient. For example, when the expected target deviation coefficient is 0.5, the corresponding degree of uncertainty is 0.142. In the worst scenario, the scheduling results obtained by this method are in line with the actual scheduling experience, which reflects the effectiveness of the method in this paper. In summary, the models and methods presented in this paper can be used to formulate optimal scheduling decision-making schemes for GVPP considering carbon trading and uncertainty.

PurposeThis study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.Design/methodology/approachIn view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.FindingsGVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.Research limitations/implicationsThis study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.Originality/valueThis study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.