Aimed at the coordination control problem of each unit caused by microgrid participation in the spot market and considering the randomness of wind and solar output and the uncertainty of spot market prices, a day-ahead real-time two-stage optimal scheduling model for microgrid was established by using the chance-constrained programming theory. On this basis, an improved particle swarm optimization (PSO) algorithm based on stochastic simulation technology was used to solve the problem and the effect of demand side management and confidence level on scheduling results is discussed. The example results verified the correctness and effectiveness of the proposed model, which can provide a theoretical basis in terms of reasonably coordinating the output of each unit in the microgrid in the spot market.
Starting from the perspective of the uncertainty of supply and demand, using the Copula function and fuzzy numbers a scenario generation method, considering the uncertainty of scenery, and a random fuzzy model of energy demand uncertainty are proposed. Then, through the energy flow direction and the energy supply, production, conversion, storage, and demand, a multi-objective model considering the economic and environmental protection of a park is constructed. Here, the park refers to a microgrid that gathers distributed energy such as wind and photovoltaics and has requirements for cooling, heat, and electricity at the same time. Next, combining the constraints of each link, the particle swarm algorithm is used to solve the model. Finally, an example is analyzed in a certain park. The results of the example show that, on the one hand, the proposed scenario generation method and fuzzy number method can reduce the uncertainty of supply and demand, effectively fitting the wind and photovoltaic output and various energy demands. On the other hand, considering the economy and environmental protection of the park at the same time, the configuration of energy storage equipment can not only improve the economy of the park, but also promote the consumption of renewable energy.
The randomness of wind generation is one of the main factors restricting gird connection of wind generation. The involvements of energy storage systems and resources used for demand response in the process of optimization for wind power are useful means to enhance its regulation capacity. Considering the uncertainty of wind generation in day-ahead plans, this paper proposes a coordinated scheduling optimization model for Wind-ES hybrid systems with demand response via electric vehicles. The model can be used to apply energy storage systems and electric vehicles simultaneously to both peak shaving/valley filling and wind generation plan tracking to achieve the coordination between the on-grid revenue and penalty cost of the hybrid system, so as to develop the optimal strategy for maximum benefits. The wind power is modeled by using scenario analysis method, and the mixed integer programming problem of this paper is solved via CPLEX software. The case study results show that the coordinated scheduling optimization model can not only earn additional revenue for electric vehicle owners, but also effectively improve the economy of wind power grid connection, which provides an important reference for scheduling the demand response resources of electric vehicles to consume wind generation.
With the increasing demand for energy, microgrid security is becoming more and more important. Therefore, how to monitor the microgrid effectively and continuously has received great attention. If the real-time on-line evaluation is realized, it is possible to improve the performance of power generation and load distribution. The current microgrid monitoring system is only to establish a monitoring center, and then send the information to the center through the equipment. However, staff need to be stationed in the monitoring center, the labor cost is too high. The scheme of microgrid monitoring system proposed in this paper makes a reasonable application of Aliyun technology. The information and data collected by the local device is passed into the gateway by specifying a unified communication protocol. The local device can provide the power, waveform and operation status of the whole power grid, in addition to displaying the power of the whole system, it also displays the power information of each element separately. The gateway then transmits the data through the optical fiber and wireless network to the CVM through the conversion protocol, and the operation of the microgrid can be monitored in real time by using external devices.
With the increasing coupling of the power system and the natural gas system, the electric–gas interconnection system has become a typical form of comprehensive energy utilization. Through the energy conversion function of the coupling unit, the system can flexibly participate in the bidding for purchasing and selling energy in a power market and a natural gas market on the premise of meeting the internal demand of multiple loads. To solve the internal coordination and optimization problem and the external flexible bidding problem in the multi-energy market, this paper proposes a robust optimization model of energy purchase and sale for the electric–gas interconnection system in a multi-energy market. Firstly, the basic structure of the electric–gas interconnection system is introduced, and the steady-state model of energy flow in the system is built based on the energy hub model. Secondly, considering the uncertainty of energy prices and the output power of renewable energy units in the system, a bidding model for energy purchase and sale of the electric–gas interconnection system in multi-energy market based on the idea of robust optimization is constructed in the framework of the Nordic energy market. Finally, empirical analysis based on the actual data is carried out, and the results prove the validity and superiority of the model. In this paper, aiming at the uncertainty of energy price, a large number of scenes are generated by Latin hypercube sampling (LHS), and then a k-means algorithm is used to reduce the scenes, so as to simulate typical scenes. Aiming at the uncertainty of the output power of the renewable energy unit in the system, a cardinal uncertainty set is used to control deviation between the actual output power and predicted output power, so that the overall robustness of the model can be controlled. The proposed model can make decision-making independent of the accurate probability distribution of uncertainty factors, and is suitable for complex multi energy systems. Meanwhile, the model possesses excellent robustness, which can effectively reduce the risk of bidding loss in the process of energy purchase and sale.
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