An Offer Strategy for Wind Power Producers That Considers the Correlation Between Wind Power and Real-Time Electricity Prices

This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.

How real time pricing modifies Chinese households’ electricity consumption

European countries are increasing their share of power production from wind and solar energy to mitigate climate change. Also the relative contributions from PV and wind power production in Europe change over time. At present, the installed capacity of photovoltaic (PV) power for all of Europe is smaller than that of wind power with a ratio of 3:4. Future scenarios for the installations of PV and wind power capacities for 2050 suggest that this ratio will substantially change. Namely, the PV power capacity might exceed the wind power capacity with a ratio of 3:2 to 2:1. We test the hypothesis that the weather dependency of anomalies in the PV plus wind power production will change in the future compared to today. Specifically, we examine which synoptic weather patterns are associated with anomalies in the PV plus wind power production for the present and future installed capacities in Europe. To that end, we developed a renewable energy model for the installed capacity of 2019 and 2050. This model allows us to simulate hourly PV and wind power production at 6 km horizontal resolution for all of Europe. We analyze the weather dependency of power-production anomalies by pairing our model output with results of the classification of weather patterns from the German Weather Service. Our results highlight similar weather patterns associated with positive anomalies in the hourly PV plus wind power production for the 2019 and 2050 installation, namely weather patterns with prevailing westerly winds. However, weather patterns associated with negative anomalies strongly change between the two installations. We also assess the dependency of the results on the duration of the production anomalies. Particularly for long production anomalies, the associated weather patterns are different for the 2019 and 2050 installation. One exception is the weather pattern Anticyclonic Southeasterly that is associated with the lowest 10-day power production in Europe for both 2019 and 2050. Regionally, weather patterns have different impacts on different regions in Europe, when comparing the associated patterns between the 2019 and 2050 installation. For instance, anomalously low power production differ for the Iberian peninsula and Southeastern Europe when the two installed capacities are compared. Taken together, our study gives a systematic overview on changes in the weather dependency of anomalies in the mix of PV and wind power between 2019 and 2050.

Wind power is one of the most rapidly growing clean and renewable energy sources. However, due to the uncertainty and intermittency of wind power, the increasing penetration of wind power into the electric power system will pose challenges to power system operators. Moreover, as a participant in a competitive electricity market, a wind power producer's behavior and profit will be influenced by other participants' behaviors. This paper proposes a model of using stochastic programming to generate optimal bidding strategies to maximize the total profits of wind and conventional power producers in both the energy market and a bilateral reserve market, where the reserve price is settled between wind and conventional power producers by using game theory. Case studies using real-world data for games in an electricity market with different types of players are performed to show the effectiveness of the proposed model.

<p indent="0mm">To achieve carbon neutrality in the power system, renewable energies (RENs), such as wind and solar power, are being rapidly installed. However, due to their strong intermittency and high uncertainty, balancing power supply and demand is becoming increasingly difficult. Time-of-use and real-time pricing (RTP) are crucial methods to ensure effective consumption of RENs, encouraging flexible resources on the demand side to work with RENs through demand-side response. Air conditioning loads represent a significant portion of urban loads, with &gt;50% of the peak load in China. They can use the building’s thermal inertia to provide regulation services, a current area of focus for demand-response research. To adapt to the RTP mechanism in the future electricity market, this study explores demand-side optimization control technology for an ice storage system in a large-scale district cooling system (DCS). An optimal control method for an ice storage system is proposed, based on compound second-sampling reinforcement learning (RL), which can effectively manage uncertainties from users’ cooling demands and real-time market prices. First, a Markov decision process (MDP) is constructed to address the operational control issue of the DCS. Second, a model-free RL algorithm is used to solve the MDP. Third, the compound second-sampling mechanism is proposed to improve training efficiency and convergence performance by combining immediate return and temporal-difference error to overcome the issue of low learning efficiency caused by uniform random sampling in the traditional RL algorithm. Finally, the experimental results confirm the effectiveness of the proposed method.

Due to uncertain nature of wind energy, the profit of a wind producer (WP) is at a notable risk. To overcome the risk associated with the uncertain wind power productions, energy storage systems have attracted an increasing attention. However, due to electricity price forecast errors, storage systems might not be able to efficiently participate in an electricity market as they both purchase and sell energy. Therefore, although the combination of WPs and storage systems can be economically promising, they require a proper offering and bidding strategy, which effectively characterizes the uncertainty associated with both wind power production and price forecast errors. To do so, this paper proposes an adaptive robust self-scheduling model for a WP paired with a compressed air energy storage system to participate in the day-ahead energy market. The proposed model addresses the uncertainties regarding the wind power productions and price forecast errors. Furthermore, the flexibility and the robustness of the proposed method can be controlled using a parameter denominating the budget of uncertainty. Simulation results illustrate the effectiveness of the proposed method.

Wind power offer strategy in day-ahead market considering price bidding strategy for electric vehicle aggregators

Building a new power system is an important measure taken by China to cope with climate change problems. Establishing a flexible and perfect electricity market and price mechanism is an important means to ensure the safe and stable operation of the power system. As one of the important electricity price mechanisms, the change in real-time electricity price (RTP) is a key factor in the operation of the electricity market, and each market participant can formulate a response strategy according to the RTP. However, the uncertain, stochastic, and fluctuant characteristics are definitely difficult problems for the RTP prediction. With the aim of solving this issue, this paper proposed a RTP prediction method based on a window-based XGBoost model. Through the input conversion of the proposed model, it can help to reduce the complexity and capture the autocorrelation effect of the RTP. The case study is conducted through the actual load data of a province in China and the superiority is proved by comparing with several state-of-art models. The result shows that the window-based XGBoost model applied in this paper can decrease the prediction error by 69.48%-95.67% and greatly enhance RTP's prediction performance.

Electricity prices based on real-time pricing (RTP) strategy provides consumers the opportunity to respond to the price changes and hence, reduce their electricity bills. Automatic home load control systems also help consumers to simply manage their usage profile more efficiently. This paper proposes a stochastic formulation for automatic home load controllers to schedule the operation period of various home appliances such that the expected electricity payment is minimized. The level of risk imposed by price volatility during the following hours is also taken into consideration in the developed formulation. Difference between the expected payment and the payment in the worst scenario is used as the risk measure. The price uncertainty is modeled via normal probability density functions with increasing standard deviations. Finally, effectiveness of the developed formulation is demonstrated by applying to a typical home.

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding, which is a kind of financial tool available in most electricity markets of the United States. In the proposed model, virtual bidding is used to improve the wind power producer's market power in the day-ahead (DA) market by trading at multiple buses, which are not limited to the locations of the wind units. The optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization model. The upper-level problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk (CVaR) for risk management. The lower-level problem represents the clearing process of the DA market. By using the Ka-rush-Kuhn-Tucker (KKT) conditions, duality theory, and big- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> method, the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints (MPEC) model and then a mixed-integer linear programming (MILP) model, which can be solved by existing commercial solvers. To reduce the computational cost of solving the proposed stochastic optimization model for large systems, a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding. Case studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual bidding. The impacts of the transmission limits, wind unit location, risk aversion parameters, wind power volatility, and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

A time-indexed integer programing approach is developed to optimize the manufacturing schedule of a factory for minimal energy cost under real-time pricing (RTP) of electricity. The approach is demonstrated using a flow shop operating during different time periods (i.e., day shift, swing shift, and night shift) in a microgrid, which also serves residential and commercial users. Results show that electricity cost can be reduced by 6.2%, 12.3%, and 21.5% for the three time periods considered, respectively. Additionally, a 6.3% cost reduction can be achieved by the residential and commercial buildings through adopting energy-conscious control strategies in this specific case study example.

Optimal Dispatch Scheduling of a Wind-battery-System in German Power Market

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments.

The intermittence of the renewable sources due to its unpredictability increases the instability of the actual grid and energy supply. Besides, in a deregulated and competitive framework, producers and consumers require short-term forecasting tools to derive their bidding strategies to the electricity market. This paper proposes a novel hybrid computational tool, based on a combination of evolutionary particle swarm optimization with an adaptive-network-based fuzzy inference system, for wind power forecasting and electricity prices forecasting in the short-term. The results from two real-world case studies are presented, in order to illustrate the proficiency of the proposed computational tool.KeywordsForecastingcomputational toolWind powerElectricity prices

Based on the current electric power market, a real-time electricity price dynamic game linkage model is established. In this paper, three cases are considered for the simulation, and a real-time electricity price is formed through the dynamic game among various participants. The result of simulation shows that large-scale energy storage system set up on the demand side improve system character. Keywords: Electric power market, Battery storage system, Real-time electricity price, Dynamic game