Energy trading and pricing in microgrids with uncertain energy supply:A three-stage hierarchical game approach

This paper studies an energy trading and pricing problem for microgrids with uncertain energy supply. The energy provider with the renewable energy (RE) generation (wind power) determines the energy purchase from the electricity markets and the pricing strategy for consumers to maximize its profit, and then the consumers determine their energy demands to maximize their payoffs. The hierarchical game is established between the energy provider and the consumers. The energy provider is the leader and the consumers are the followers in the hierarchical game. We consider two types of consumers according to their response to the price, i.e., the price-taking consumers and the price-anticipating consumers. We derive the equilibrium point of the hierarchical game through the backward induction method. Comparing the two types of consumers, we study the influence of the types of consumers on the equilibrium point. In particular, the uncertainty of the energy supply from the energy provider is considered. Simulation results show that the energy provider can obtain more profit using the proposed decision-making scheme

A three-stage optimal operation strategy of interconnected microgrids with rule-based deep deterministic policy gradient algorithm

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.In part by the National Natural Science Fund, the Basic Research Project of Leading Technology of Jiangsu Province, the National Natural Science Fund of Jiangsu Province and the National Natural Science Key Fund.https://ieeexplore.ieee.org/servlet/opac?punumber=5962385hj2023Electrical, Electronic and Computer Engineerin

Resilient optimal defensive strategy of TSK fuzzy-model-based microgrids' system via a novel reinforcement learning approach

With consideration of false data injection (FDI) on the demand side, it brings a great challenge for the optimal defensive strategy with the security issue, voltage stability, power flow, and economic cost indexes. This article proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. Due to FDI uncertainty of the system load, TSK-based deep deterministic policy gradient (DDPG) is proposed to learn the actor network and the critic network, where multiple indexes' assessment occurs in the critic network, and the security switching control strategy is made in the actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with online coordination between the actor network and the critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, the penalty-based boundary intersection (PBI)-based multiobjective optimization method is utilized to solve economic cost and emission issues simultaneously with considering voltage stability and rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problems on interconnected microgrids.In part by the National Natural Science Fund, the Basic Research Project of Leading Technology of Jiangsu Province, the National Key Research and Development Program of China and the National Natural Science Key Fund.https://ieeexplore.ieee.org/servlet/opac?punumber=5962385hj2023Electrical, Electronic and Computer Engineerin

An Improved Droop Control Strategy Based on Changeable Reference in Low-Voltage Microgrids

This paper proposes an improved droop control strategy based on changeable reference in low-voltage microgrids. To restore running frequency of distributed generation to a rated value without affecting its reactive power output, changeable frequency reference, mainly compensating for frequency deviation, are proposed corresponding to various load demands. In terms of active power sharing inaccuracy associated with mismatched line impedance, changeable voltage amplitude reference is proposed to obtain a droop line suitable for the actual voltage of distributed generations. By further improvement of the active droop coefficient, power sharing is accurate with a difference in actual voltages of distributed generations. Virtual negative inductance is used to neutralize the redundant line inductance for strictly improving sharing accuracy. A robust control method based on Lyapunov function is used to handle the robustness problem in case of load variation. The control scheme is entirely decentralized, so communication links among distributed generations are redundant. Finally, simulation studies demonstrate the effectiveness of a control strategy

Optimal Scheduling Strategy of Distribution Network Based on Electric Vehicle Forecasting

Based on the Monte Carlo method, this paper simulates, predicts the load, and considers the travel chain of electric vehicles and different charging methods to establish a predictive model. Based on the results of electric vehicle simulation prediction, an optimal scheduling model of the distribution network considering the demand response side load is established. The firefly optimization algorithm is used to solve the optimal scheduling problem. The results show that the prediction model proposed in this paper has a certain reference value for the prediction of an electric vehicle load. The electric vehicle is placed in the optimal scheduling resource of the distribution network, which increases the dimension of the scheduling resources of the network and improves the economics of the distribution network operation

Investigation of temperature and deformation of mold copper plate in slab continuous casting

Based on the cooling parameters of the mold copper plate for slab continuous casting in a steelmaking plant, the three-dimensional calculating model of the copper plate was established, and three-dimensional distributions of temperature, thermal stress and strain were simulated numerically by using finite element method (FEM). The maximum deformation of the mold copper plate, the highest temperature and thermal stress were obtained. This research is useful for the structure design of the mold