Aiming for large-scale renewable energy sources (RES) integrated to power systems with power electronic devices, the technology of virtual synchronous generator (VSG) has been developed and studied in recent years. It is necessary to analyze the damping characteristics of the power system with RES generation based on VSG and develop its corresponding damping controller to suppress the possible low frequency oscillation. Firstly, the mathematical model of VSG in a per unit (p.u) system is presented. Based on the single-machine infinite bus system integrated with an RES power plant, the influence of VSG on the damping characteristics of the power system is studied qualitatively by damping torque analysis. Furthermore, the small-signal model of the considered system is established and the damping ratio of the system is studied quantitatively by eigenvalue analysis, which concluded that adjusting the key control parameters has limited impacts on the damping ratio of the system. Consequently, referring to the configuration of traditional power system stabilizer (PSS), an auxiliary damping controller (ADC) for VSG is designed to suppress the low frequency oscillation of the power system. Finally, simulations were performed to verify the validity of theoretical analysis and the effectiveness of designed ADC.

In a competitive electricity market with substantial involvement of renewable electricity, maximizing profits by optimizing bidding strategies is crucial to different power producers including conventional power plants and renewable ones. This paper proposes a game-theoretic bidding optimization method based on bi-level programming, where power producers are at the upper level and utility companies are at the lower level. The competition among the multiple power producers is formulated as a non-cooperative game in which bidding curves are their strategies, while uniform clearing pricing is considered for utility companies represented by an independent system operator. Consequently, based on the formulated game model, the bidding strategies for power producers are optimized for the day-ahead market and the intraday market with considering the properties of renewable energy; and the clearing pricing for the utility companies, with respect to the power quantity from different power producers, is optimized simultaneously. Furthermore, a distributed algorithm is provided to search the solution of the generalized Nash equilibrium. Finally, simulation results were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game-based bi-level optimization approach.

Bilateral contract transaction among generation companies and large consumers is attracting much attention in the electricity market. A large consumer can purchase energy from generation companies directly under a bilateral contract, which can guarantee the economic interests of both sides. However, in pursuit of more profit, the competitions in the transaction exist not only between the company side and the consumer side, but also among generation companies. In order to maximize its profit, each company needs to optimize bidding price to attract large consumers. In this paper, a master–slave game is proposed to describe the competitions among generation companies and large consumers. Furthermore, a Bayesian game approach is formulated to describe the competitions among generation companies considering the incomplete information. In the model, the goal of each company is to determine the optimal bidding price with Bayesian game; and based on the bidding price provided by companies and the predicted spot price, large consumers decide their personnel purchase strategy to minimize their cost. Simulation results show that each participant in the transaction can benefit from the proposed game.

With the increasing penetration of renewable energy in power systems, fluctuation of renewable energy power plants has great influence on stability of the system, and renewable power curtailment is also becoming more and more serious due to the insufficient consumptive ability of local power grid. In order to maximize the utilization of renewable energy, this paper focuses on the generation scheduling optimization for a wind-solar-thermal hybrid system considering that the produced energy will be transmitted over a long distance to satisfy the demands of the receiving end system through ultra-high voltage (UHV) transmission lines. Accordingly, a bilevel optimization based on a non-cooperative game method is proposed to maximize the profit of power plants in the hybrid system. Users in the receiving end system are at the lower level of the bilevel programming, and power plants in the transmitting end system are at the upper level. Competitive behavior among power plants is formulated as a non-cooperative game and the profit of power plant is scheduled by adjusting generation and bidding strategies in both day-ahead markets and intraday markets. In addition, generation cost, wheeling cost, and carbon emissions are all considered in the non-cooperative game model. Moreover, a distributed algorithm is presented to obtain the generalized Nash equilibrium solution, which realizes the optimization in terms of maximizing profit. Finally, several simulations are implemented and analyzed to verify the effectiveness of the proposed optimization method.

It is necessary to model photovoltaic generation system based power system electromechanical transient time scales for large-scale PV connected to power system stability analysis. The model should reflect the non-linear output characteristics, fault ride-through response characteristics and output limits of photovoltaic generation system. A PV model used to meet these demands is proposed in this paper. Base on a 3-generator, 9-bus power system, the comparison and verification of the model is carried out in both DIgSILENT/PowerFactory and PSASP simulation environment