5/3/2023 0 Comments Region x vpppa conference![]() This is Jeff Dalto, Senior Learning & Development Specialist with Vector Solutions, doing another one of our semi-regular webinar/interview/podcast series, and today we have an exciting guest. Finally, the effectiveness of the model is verified by taking the IEEE30-bus system as an example.Introduction to Mark Hurliman, VPP, and SHARP The dynamic game is repeated until the optimal equilibrium solution is obtained. Each agent renews the game to update the bidding strategy based on the clearing result and the reporting of the subagents. The agent uses genetic algorithms to determine the optimal bid strategy, and the TC carries out market clearance with the goal of maximizing social benefits according to the quotation results. The game is played with the goal of maximum self-interest. The trading center (TC) is the leader and VPP is the agent and the follower. The upper layer is the external market bidding game. Then, the subagents renew the game to update the bidding strategy based on the outcomes of the external and internal markets. Each subagent uses the particle swarm algorithm (PSA) to determine the optimal offer coefficient, and VPP carries out internal market clearing with the minimum variance of unit profit according to the quoting results. VPP is the leader and each DER is a subagent that acts as a follower to maximize its profit. The lower layer is a bidding game for VPP internal market including DER. Secondly, using multi-agent technology and Stackelberg dynamic game theory, a double-layer nested dynamic game bidding model including VPP and its internal DERs is designed. Firstly, the basic concept of VPP is outlined, and various uncertainties within VPP are modeled. It also provides an important way for distributed energy resources (DER) to participate in electricity market transactions. The proposed method can get the scheduling scheme with the lowest operating cost in the worst scenario and is conducive to reducing the overall scheduling cost of the system.Īs renewable energies become the main direction of global energy development in the future, Virtual Power Plant (VPP) becomes a regional multi-energy aggregation model for large-scale integration of distributed generation into the power grid. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Based on the dual transformation theory and the column constraint generation algorithm, the original model was solved alternately. ![]() A two-stage robust stochastic optimal model of the min-max-min structure was established. For the load side uncertainties, the Wasserstein generative adversarial network with gradient penalty is used to generate electric, thermal, cooling, and natural gas load scenarios, and the K-medoids clustering is used to get typical scenes. For the source side uncertainties, the uncertain set of cardinalities with a robust adjustable coefficient is adopted to describe the output of wind turbines and photovoltaics. Here we propose a robust stochastic optimal dispatching method to solve the scheduling problem under multiple uncertainties. At the same time, insufficient research on optimal scheduling of multi-energy virtual power plants under multiple uncertainties. In recent years, with the rapid development of the energy Internet and the deepening of the complementary coupling of various energy sources, the concept of multi-energy virtual power plant comes into being. The simulation results show that the proposed model and method can provide peak load shifting capacity and improve renewable energy consumption, effectively reducing the cost and carbon emission of VPP. In view of the high dimensional nonlinearity of the proposed optimization model and the difficulty in solving it, a new Gaussian compound differential evolution algorithm is designed to solve the model. In addition, the power consumption of the carbon capture and the flue gas treatment can be transferred through joint dispatching to smooth the fluctuation of renewable energy output, so that the wind power and photovoltaic can be indirectly dispatchable and flexibly utilized. By introducing the collaborative utilization framework with a carbon capture plant-power to gas (P2G)-gas unit system, the CO2 in the carbon capture can be used as the raw material of P2G, which produces natural gas to be supplied to the gas unit. In order to promote multi-energy complementation and low carbonization, an optimal scheduling model of virtual power plant (VPP) with carbon capture and waste incineration considering power-to-gas coordination is proposed.
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