Day-ahead Research Articles

Virtual Power Plant’s Optimal Scheduling Strategy in Day-Ahead and Balancing Markets Considering Reserve Provision Model of Energy Storage System

In recent years, with the rapid increase in renewable energy sources (RESs), a Virtual Power Plant (VPP) concept has been developed to integrate many small-scale RESs, energy storage systems (ESSs), and customers into a unified agent in the electricity market. Optimal coordination among resources within the VPP will overcome their disadvantages and enable them to participate in both energy and balancing markets. This study considers a VPP as an active agent in reserve provision with an upward reserve capacity contract pre-signed in the balancing capacity (BC) market. Based on the BC contract’s requirements and the forecasted data of RESs and demand, a two-stage stochastic optimization model is presented to determine the VPP’s optimal scheduling in the day-ahead (DA) and balancing energy (BE) markets. The probability of reserve activation in the BE market is considered in this model. The ESS’s reserve provision model is proposed so as not to affect its schedule in the DA market. The proposed optimal scheduling model is applied to a test VPP system; then, the effects of the BC contract and the probability of reserve activation on the VPP’s trading schedule are analyzed. The results show that the proposed model has practical significance.

Blockchain-Based Renewable Energy Trading Using Information Entropy Theory

Renewable energy sources (RES) and electric vehicles (EVs) are widely recognized as primary ways to reduce carbon emissions and essential components of low-carbon power systems. However, both of them have strong uncertainties which bring great challenges to power transactions and the operation of power grids. This paper defines the uncertainty cost of wind power producer(WPP) in day-ahead(DA) market pricing based on information entropy theory for the first time and proposes a EV charging management strategy with DA contract. The constructed low-carbon emission electricity market(LCEM) quantifies the uncertainty cost and contracts the disordered charging of EVs. It reduces the uncertainty and ensures the balance of power supply and demand. In addition, the transaction between WPP and EVs is described as the Stackelberg game, and its communication network is constructed through a blockchain network to ensure transaction efficiency and privacy security. Experiments show that LCEM can accurately measure the uncertainty of wind power generation, increase the net profit for WPP by more than 2% when the prediction error is greater than 10%, and adopt EV contract charging management in the DA market to minimize charging costs.

A Market Strategy for Joint Profitability of Electric Vehicle Charging Stations and Retailers: Considering Electricity Price Fluctuations and Users’ Service Fee Response

With the introduction of the “carbon peaking and carbon neutralizing” initiative, China is undergoing a positive energy transition, emphasizing the replacement of traditional fossil fuels with clean energy sources. As part of this transition, numerous megacities are progressively endorsing electric vehicles (EVs) as substitutes for traditional fuel‐based vehicles. The expanding use of EVs, however, has presented challenges related to the randomness and volatility of EV charging. In response to this, many regions in China have mandated the implementation of the time‐of‐use (ToU) scheme. This scheme divides the day into distinct periods such as “valley,” “flat,” “peak,” and “critical peak,” each associated with varying charging prices. According to the management regulations, the fees collected from vehicle owners at the charging station are divided into two parts: ToU electricity value and service fees. The former is jointly determined by the power company and the government, while the latter is set by the charging station operator. To overcome the limitation on profitability, this paper proposes a scheme enabling charging stations and retailers to participate in the day‐ahead (DA) market. The proposed approach involves a two‐stage decision‐making method. By analyzing the charging habits of local residents, the paper constructs a quadratic curve for dynamic service fees and charging rates. The objective function is then utilized to adjust the anticipated charging load curve. Prior to the DA‐bidding process, an uncertain set of scenarios for electricity prices and charging loads is generated. The conditional value‐at‐risk (C‐VaR) method is employed to balance expected benefits and risks amid uncertain scenarios.

A smart predict-and-optimize framework for microgrid's bidding strategy in a day-ahead electricity market

This paper deals with the microgrid's bidding strategy (MGBS) problem in a day-ahead (DA) electricity market. To this end, the DA electricity prices, demand, and renewable energy uncertainties are modeled by different scenarios via generating several representative daily curves. However, the real-time (RT) electricity prices are modeled using robust optimization (RO) via predicting appropriate intervals. The existing paradigm to predict the RT intervals is to minimize the statistical errors, while this accuracy-oriented method may not necessarily yield the best MGBS plan against the actual realization of RT prices. The novelty of this research is to present a smart predict-and-optimize (SPO) methodology in which a cost-oriented prediction model replaces the existing accuracy-oriented methods. Trilevel mathematical programming is established to construct a cost-oriented model where the first level maximizes/minimizes the actual profit/cost by adjusting the predicted interval bounds of the RT prices. Knowing these trained interval bounds, the second level runs a scenario-based DA-MGBS. Then, a rescheduling problem is solved at the third level based on the actual realization of uncertainties. To solve this trilevel problem, a reformulation-and-decomposition (R&D) algorithm is utilized. The numerical experiment illustrates the advantages of the SPO method in terms of the microgrid (MG) cost.

A Medium-Term Virtual Power Plant Optimization Framework Considering the Failure Rate of Its Intermittent Units Using Stochastic Programming

This article presents a model for maximizing the profit of a commercial virtual power plant (VPP) comprised of heterogeneous distributed energy resources (DERs) considering the failure or power outage of its intermittent units. The failures are taken into account through scenarios of failure happening in different parts of the units with different probabilities. The VPP has access to the future market, the day-ahead (DA) market, and bilateral contracts for trading. Since some of the parameters such as the DA market prices are volatile and uncertain, a two-stage stochastic programming approach is developed to simulate the uncertainty effectively. The VPP makes decisions regarding the future market and signing bilateral contracts in the first stage, then, decisions regarding trading in the DA market and the operation of the VPP’s DERs are taken in the second stage. Using the conditional value at risk (CVaR) approach, the behavior of the risk-neutral VPP is compared to the risk-averse VPP. It is shown that considering the failure of intermittent generation units of the VPP leads to more sensitivity of its profit toward pool prices for both risk-neutral and risk-averse VPPs. It also leads to at least 3.4% of VPP’s profit lost in the specific designed case study.

Carbon-Oriented Electricity Balancing Market for Dispatchable Generators and Flexible Loads

The high renewables penetration results in increased imbalance volumes and balancing actions due to system stability requirements. The balancing market (BM) primarily turns down renewable generation and turns up traditional carbon-intensive generation in response to real-time energy imbalance. Existing dual-stage market mechanisms conflict with the carbon reduction trajectory by implementing balancing actions regardless of their carbon footprint. This paper proposes a novel carbon-oriented BM model to coordinate the environmental targets in the dayahead (DA) and real-time BMs. The emissions of dispatchable generators and flexible loads are distinguished by their operation modes and flexibility types, respectively. Carbon signals are incorporated into their bid/offer prices through the proposed carbon emission flow (CEF) model. By integrating these carbon incentives, the dual-stage market model is formulated to minimize economic and environmental costs. Simulation results demonstrate that, overall, although the proposed BM mechanism results in an increased cost of balancing services (159.10 m£), there is a concurrent larger drop in carbon costs (294.14 m£), resulting in a reduction in total cost. It enables system operators to incentivize decarbonized energy resources in DA scheduling and real-time balancing actions.

Nash Equilibrium Estimation and Analysis in Joint Peer-to-Peer Electricity and Carbon Emission Auction Market With Microgrid Prosumers

The joint Peer-to-Peer (P2P) electricity market (EM) and carbon emission auction market (CEAM) among prosumer microgrids (MGs) in the distribution network is a promising paradigm to facilitate the participation of distributed energy resources (DERs) and incentivize the decarbonization. In this market, MGs will modify their bidding strategies to be adaptive to other rival MGs' for profit maximization. Such modification will converge to the Nash Equilibrium Point (NEP), where each MG cannot obtain more profits by modifying its strategy subject to the fixed strategy of other rival MGs. In this paper, the NEP under such a joint market paradigm is investigated, in which MGs will trade electricity in the EM and purchase carbon emission quotas (CEQs) in the CEAM. In addition, MGs must adjust their bidding strategies considering penalties due to deviations between day-ahead (DA) scheduling and real-time (RT) procurement caused by uncertainties of net load, as well as the price fluctuation in the CEAM. The NEP is estimated by a novel Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and the risk mitigation is achieved by incorporating the conditional value-at-risk (CVaR) constraint. The computational performance and effectiveness of risk mitigation of this proposed algorithm, and the obtained NEP in the joint EM and CEAM, are analyzed in the case studies.

Light robust co-optimization of energy and reserves in the day-ahead electricity market

To accommodate the stochasticity of variable renewable energy sources (VRES) while efficiently dispatching generation resources and procuring adequate reserves, previous research proposed co-optimizing energy and reserves in the day-ahead (DA) using various uncertainty-based mechanisms. However, the co-optimized markets based on these mechanisms exhibit implementation limitations related to their high computational burden, complex customized solution algorithms, and over-conservative solutions. To address these shortcomings, this paper proposes a practical light robust optimization (LR) approach for the DA co-optimization of energy and reserves. The method results in a linear market clearing mechanism that easily enables the control of the robustness level of the solution through a tunable conservativeness parameter. In addition, the paper explores three different formulations for specifying the system reserve requirements considering, namely, fixed reserve requirements (LRF1), variable reserve requirements based on system uncertainty (LRF2), and a combined approach (LRF3). The formulations integrate the uncertainty from VRES in the market setting using a new bid format called uncertainty bid. The three formulations are then compared using a case study. The numerical results show the effects of the variation of the conservativeness parameter and the reserve requirements on the total socio-economic welfare (SEW), dispatched energy quantities, anticipated activation costs, and procured reserves. Moreover, the analyses showcase that sizing reserves based on system uncertainty (in LRF2) results in a 27%–61% decrease in reserve procurement costs when compared with LRF1, while the combined approach (in LRF3) results in a better performance than LRF2 in terms of reserve activation costs, with costs 61%–263% lower than in LRF2.

Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach

The uncertainties induced by electric vehicle (EV) demand and market operations pose huge challenges to the optimal bidding decision of the EV aggregator (EVA) in the day-ahead (DA) market. Note that a risk-neutral bidding solution with the expected cost minimization may make the EVA suffer a high financial loss in the market. As such, in this study, a risk-averse bidding strategy is developed to support the EVA to participate in the market via the conditional value-at-risk (CVaR) to handle market price volatility. Specifically, the strategy minimizes the CVaR metric over a collection of real-time (RT) clearing scenarios to reduce the energy transaction risk of the EVA in the market. Moreover, the model is reformulated as a linear programming (LP) problem that is mathematically tractable and can be efficiently solved. The proposed solution is extensively assessed through experiments based on the PJM market against the risk-neutral bidding strategy as a comparison benchmark. The numerical results reveal that the proposed risk-averse bidding strategy outperforms the risk-neutral one in terms of risk control, which enables the EVA to avoid suffering a huge financial loss incurred by RT clearing prices. In addition, the transformed LP model is superior to the original model in computational efficiency.

A multi-stage data-driven IGDT-RO model with chance compensation for optimizing bidding of RES aggregator in competitive electricity markets

Driven by environmental policies and cost reduction efforts, renewable energy sources (RESs) become increasingly popular worldwide. It is a promising way to integrate dispersed RESs in the form of aggregators into the electricity market. This paper focused on collaborative bidding for an aggregator that integrates wind, solar, hydropower and energy storage system (ESS) in day-ahead (DA) and intraday (ID) markets. We propose a comprehensive data-driven based information gap theory-robust (DIGDT-RO) to handle the multi-stage optimal bidding for the RES aggregator. The RO approach is presented to model the uncertainty of ID electricity price, while uncertainties related to wind and solar generation are considered in DIGDT, which allows the aggregator to adopt risk-averse or risk-seeking strategies towards generation fluctuations. In DIGDT, the forecasted error of wind and solar is estimated by a novel confidence interval-based ambiguity set construction method (CIAS), and then the possibility of hydropower and ESS compensating for power deviation is modeled by chance constraints. The numerical results verify the good profitability and superior adaptability of the proposed model towards uncertainties.

A stochastic analysis of the energy and reserve operation for battery storage-assisted prosumer aggregator in the Southwest Power Pool Market

To facilitate the integration of renewable energy in electric power systems, the installed capacity of distributed renewable energy sources has been increased rapidly worldwide in recent years, and the prosumers and prosumer aggregators have become important players in smart grid. To maximize the economic benefits of a prosumer aggregator in power markets, a stochastic model used for generating the day-ahead (DA) energy and operating reserve scheduling strategies is proposed. The uncertainties associated with solar power productions, demands and prices in energy and operating reserve markets are handled via stochastic optimization technique. To manage the potential risks faced by the prosumer aggregator, the conditional value at risk is adopted to measure and manage its potential low profits or losses. Finally, case studies are performed by using the data in the energy and operating reserve markets operated by the Southwest Power Pool (SPP), and the optimal energy and operating reserve bids are generated effectively by solving the proposed model. The values of the operating reserve markets and battery storages are analyzed in detail via simulation, and it shows that higher reserve prices and larger battery storage capacity would lead to higher expected profit and lower risks for the prosumer aggregator.

Optimal multi-market operation of gravity energy storage and wind power producer using a hybrid stochastic/robust optimization

In light of physical limitations, the well-known large-scale pump hydro energy storage was unable to take place in predominantly flat areas. The utilization of innovative gravity energy storage (GES) has increased in the power system in the past few years, with no geographical restrictions and reduced investment costs. According to the paucity of installed GES, a proper formulation considering the physical specifications of gravity energy for market participants in GES has not been sufficiently investigated. In this paper, a new non-linear formulation for the strategic multi-market running of GES and wind suppliers in an uncertain environment characterized by joint robust optimization (RO) and stochastic programming is introduced. Using piecewise linearization, the proposed nonlinear model is then reformulated as a linear version. In the proposed method for market involvement, the bid-offer plans of the GES are obtained in order to participate actively in the DA market. Obtained results show that market conditions significantly impact the profitability of the system, with a 30 % reduction in the day-ahead (DA) price leading to a 32 % reduction in daily profit. Also the risk-based operation of the GES and wind producer is compared with risk-neutral condition, highlighting the importance of considering intraday and real-time markets and risk profiles in optimizing the operation and profitability of the hybrid wind-GES system.

A multi-time scale coordinated control and scheduling strategy of EVs considering guidance impacts in multi-areas with uncertain RESs

With the increased electrification of transportation sector, the electric vehicles (EVs) are deemed to be key players in energy scheduling act to realize more economical operation of distribution networks. EVs have the function of energy space–time transfer, and energy-space coupling effects need to be considered in scheduling. In this paper, EVs’ spatial characteristics from multi-areas and characteristics of transferable charging power are both taken into account, then a multi-time scale coordinated control and scheduling strategy is proposed to achieve optimal schedules in both day-ahead (DA) and real-time (RT) periods. First, in DA periods, EVs are modeled as shiftable and location-flexible loads to participate in multi-areas’ scheduling task managed by a unified distribution system operator (DSO). To attract more spatially distributed EVs to different charging stations and avoid charging congestion, a price-based transfer model (PBTM) is established to realize EVs charging guidance in different areas while integrated into the DA stochastic scheduling. Next, in RT periods, EVs are modeled as controllable loads to compensate RT power errors caused by uncertain renewable energy sources (RESs) and inaccuracies associated with DA prediction. Both DA and RT scheduling are coordinated with a RT control strategy for EVs, in which a state space model (SSM) is constructed to calculate charging power and then form 1-min control signals to realize the tracking of multi-time scale schedule. Simulation results demonstrate that the proposed coordinated control and scheduling strategy can guide more EVs to be grid-connected, promote multi-time scale economic scheduling, and benefit the EV users meanwhile.

A risk-averse two-stage stochastic model for optimal participation of hydrogen fuel stations in electricity markets

In this paper, a novel stochastic risk-averse mixed-integer linear programming (MILP) model is developed for optimal electricity procurement of hydrogen fuel stations (HFSs) with responsive hydrogen demands. In the developed model, HFS operator may procure its required electricity through day-ahead (DA) market, bilateral contracts, a contract with withdrawal penalty (CWP) and balancing market. The HFS is committed to inject a pre-specified volume of hydrogen into a natural gas network. In the developed stochastic model, the uncertainties in hydrogen demand and DA market prices are characterized as random variables and modeled as scenarios. The developed model is a two-stage model in which the decisions on transactions with bilateral contracts are the first-stage decision variables and the other decision variables belong to the second stage. The risk of suffering from high procurement costs is modeled using the Conditional Value at Risk (CVaR). The results confirm the efficiency of the proposed model both in terms of reduction in HFS expected cost and risk alleviation. The results show that the transaction through bilateral contracts decreases the expected cost and CVaR respectively by 6% and 3%. The results also indicate that the participation in the balancing market decreases the expected cost and CVaR respectively by 6% and 1.6%. As per results, in scenarios in which DA market prices are lower than CWP prices, HFS operator prefers to pay a penalty fee and withdraw from CWP. The sensitivity of HFS profit to the weight factor of risk metric and the participation factor of responsive demands are assessed.

Decentralized energy management system of distributed energy resources as virtual power plant: Economic risk analysis via downside risk constraints technique

A virtual power plant (VPP), as a decentralized energy management system (EMS), can be a handy application to aggregate and schedule distributed energy resources (DER). The DERs can be dispatchable and non-dispatchable energy resources wishing to participate in the day-ahead (DA) electricity market. Therefore, they can provide the grid with higher reliability and safety margins. However, there are uncertainties in the market that VPP should manage and the associated risks to obtain robust scheduling in such a probabilistic environment. In this paper, risk-oriented stochastic optimization is proposed to investigate the market's uncertainties and obtain the maximum profit while ensuring the safety margins of the network. Downside risk constraints (DRC) are integrated into the model to cover the risks. So, the risk-based scheduling of VPP is obtained. The proposed risk-based framework is formulated as the mixed-integer linear programming (MILP) model, solved by the CPLEX solver of GAMS software. The proposed model is applied to an 18-bus test system to investigate the proposed risk-oriented stochastic optimization of VPP. Finally, the costs, revenues, and total profit are illustrated, compared, and analyzed in the model's risk-neutral (RN) and risk-averse (RA) states so that an appropriate decision can be made. Moving toward the RA strategy lowers the amount of generated power of the DG and the purchased power from the upstream grid, consequently leading to a lower amount of sold power by the VPP within the studied power grid. The analysis shows that the RA strategy decides with low risk by decreasing the cost and revenue of the VPP simultaneously. In this regard, the results indicate that the average profit of the VPP decreased by 8.62%, from $15407 to $14079 when decision-maker tends to take risk-averse decisions with zero risk.

Optimal Participation of Co-Located Wind–Battery Plants in Sequential Electricity Markets

Since hybrid power plants (HPPs) play an intensive role in the energy supply balance of future energy systems, there is today increased attention on co-located wind–battery HPPs both in industry and academia. This paper proposes an energy management system (EMS) methodology for wind–battery plants participating in two sequential electricity markets, namely in the spot market (SM) and the balancing market (BM). The proposed and implemented EMS consists of day-ahead (DA) spot market optimization, hour-ahead (HA) balancing market optimization, and intra-hour re-dispatch optimization to allow HPPs to achieve energy arbitrage, to offer regulation power at the HA stage, and to reduce real-time imbalances. The optimization models used in the EMS incorporate an accurate battery degradation model and grid connection constraints. This paper presents a detailed case analysis of the profitability of HPPs in markets towards 2030 based on the proposed EMS. Furthermore, the value of intra-hour re-dispatch optimization in improving the feasibility of generation plans, as well as the impacts of overplanting on wind energy curtailment and battery degradation, is also investigated based on the proposed EMS.

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

A scheduling framework for VPP considering multiple uncertainties and flexible resources

This paper presents a two-stage scheduling framework for a virtual power plant (VPP) to address uncertainties in wind power plants (WPP), photovoltaic (PV), load, and price in the power system. The framework consists of a day-ahead (DA) scheduling stage and an intraday adjustment stage. The DA scheduling stage formulates the VPP's internal controllable resource scheduling strategy and the energy market's bidding plan. The intraday adjustment stage provides flexibility to adjust the scheduling strategy of controllable resources in the VPP to enhance power generation stability. To address profit distribution issues, the paper proposes a profit distribution strategy based on scheduling costs of each component unit of the VPP. The VPP considers various factors, such as battery capacity degradation, carbon credit trading, flexibility of electric vehicles (EVs), and novel demand response (DR) strategies. The case study demonstrates that accounting for battery capacity degradation reduces the utilization of energy storage systems (ESSs) and EVs, while considering controllable load and EVs increases the VPP's flexibility and decreases carbon emissions. Furthermore, the proposed two-stage scheduling strategy effectively handles multiple uncertainties. Finally, the implementation of new DR strategies leads to a reduction in cost by 6.83% (time-of-use) and 8.1% (real-time price) for the VPP.

A three-layer game theoretic-based strategy for optimal scheduling of microgrids by leveraging a dynamic demand response program designer to unlock the potential of smart buildings and electric vehicle fleets

The proliferation of the number of Smart Buildings (SBs) and the fleet of Electric Vehicles (EVs) in Distribution Systems (DSs) makes the need for new strategies to coordinate them with microgrid (MG) scheduling inevitable. Therefore, this article proposes a three-layer risk-averse game theoretic-based strategy to coordinate SBs and EV fleets with MGs scheduling. In the first layer of this strategy, a Demand Response Program (DRP) is designed for SBs where dynamic incentive tariffs are calculated based on the consumption pattern of subscribers. Then, in the second layer, the scheduling of SBs and EV fleets is done in a decentralized space and considering their participation in the designed DRP. Eventually, in the third layer, the operators of MGs have received the power exchange information of SBs in order to carry out their scheduling in accordance with it. In this layer, the Day-Ahead (DA) scheduling of MGs and DS is done through the implementation of a cooperative game theory. Fluctuations of uncertain operational parameters such as load demand, radiation and wind are embedded in the model by scenario-based technique where a Risk-Averse (RA) strategy is adopted to manage them. Running the proposed three-layer strategy on a 69-node DS containing four MGs showed that this strategy can use the potential of SBs and EV fleets to improve the voltage characteristics in the high-demand period and reduce total daily costs by 13.66% with designing a dynamic-tariff DRP. Moreover, the results reveal that MGs using the Peer-to-Peer (P2P) power exchange option have not only reduced the power losses in the system but also reduced the total daily costs by about 8%.

Optimal Offering Strategy of GenCo With Joint Participation in FTR Auction and Day-Ahead Market Considering Virtual Bidding

Nodal price separations in Day-Ahead (DA) market caused by transmission congestion create congestion charges/surplus that are reallocated to the market participants through the financial transmission right (FTR) auction. From a market participant's viewpoint, these two markets are interrelated because the revenue of market participant in FTR auction is determined based on the day-ahead locational marginal prices. Furthermore, virtual transactions which are designed to improve price convergence between the day-ahead and real-time markets can directly impact day-ahead prices. This impact through virtual transactions may be utilized by a market participant to increase its FTR value and improve its overall strategy in participating in both FTR auction and day-ahead market. To this end, this paper is the first attempt to reveal this tactic in literature and proposes an offer strategy framework for a price-maker generating company participating in both FTR auction and day-ahead market with the consideration of virtual bidding. First, the possibility of FTR value manipulation is conceptually demonstrated by placing virtual bids in the day-ahead market. Second, a two-stage bi-level offering strategy model is formulated for strategic GenCos, which is further converted to a single level optimization problem by using Karush-Kuhn-Tucker conditions and strong duality theory. Numerical tests on a 5-bus system and IEEE reliability test system (RTS) demonstrate the effectiveness and applicability of the proposed approach.