Yuanzheng Li

Distributed Robust Energy Management of a Multi-Microgrid System in the Real-Time Energy Market

In this paper, a distributed robust energy management scheme for multiple interconnected microgrids (MGs) is developed. It aims to optimize the total operational cost of the MGs through energy trading with neighboring MGs and the main grid in the real-time energy market. Various uncertainties including renewable generation, load consumption, and buying/selling prices of the main grid are handled using an adjustable robust optimization technique. To keep consistent with the distributed nature of the multiple MGs, we propose a distributed adjustable robust optimal scheduling algorithm. Within the framework, each MG energy management system determines its own selling price and operation schedule via distributed communication of noncritical information with its neighboring MGs. Robust optimal scheduling and fair energy trading can be collectively achieved. A case study of a 4-MG system is conducted to validate the effectiveness of the proposed approach.

Optimal Operation of Multimicrogrids via Cooperative Energy and Reserve Scheduling

Microgrid (MG) represents one of the major drives of adopting Internet of Things for smart cities, as it effectively integrates various distributed energy resources. Indeed, MGs can be connected with each other and presented as a system of multimicrogrid (MMG). This paper proposes the optimal operation of MMGs by a cooperative energy and reserve scheduling model, in which energy and reserve can be cooperatively utilized among MMGs. In addition, values of Shapely are introduced to allocate economic benefits of the cooperative operation. Finally, a case study based on a system of MMGs is conducted, and simulation results verify the effectiveness of the proposed cooperative scheduling model.

An interactive decision making model based on energy and reserve for electric vehicles and power grid using generalized Stackelberg game

This paper proposes an interactive decision making model for electric vehicles (EVs) and power grid, in the perspective of energy and reserve. In this model, EVs optimize their energy and reserve plans according to prices set by the power grid. Meanwhile, the power grid aims to obtain the optimal energy and reserve prices for EVs, in order to maximize its benefits. Then we use the generalized Stackelberg game (GSG) to formulate the interactive decision making model, and prove the existence and uniqueness of the generalized Stackelberg equilibrium. Simulation results verify that the proposed interactive model can well improve benefits of EVs, while guaranteeing their quality of charging services.

Day-ahead coordinated scheduling of hydro and wind power generation system considering uncertainties

This paper presents a day-ahead coordinated scheduling method of hydro and wind power generation system (HWPGS) with consideration of uncertainties. In this method, we first formulate the coordinated scheduling model with two-part price and power flow constraints, which can well manifest the fixed and variable economic benefits as well as the secure operation of HWPGS, respectively. Then, interval analysis is adopted to study effect of uncertain wind power, water inflow, and electricity load on the coordinated scheduling model. Finally, we use a real test system in a western area of China to conduct simulation studies, which verify the effectiveness of interval analysis and the effect of price mechanism on the day-ahead coordinated scheduling of HWPGS.

Optimal reactive power dispatch control considering uncertain wind power

In this paper, a downside risk constrained optimal reactive power dispatch control (ORPDC) model is proposed, with the consideration of uncertain wind power integrated into power systems. In this model, the expectation of transmission loss and its downside risk are considered, simultaneously. In this way, the proposed model is formulated as a multi-objective optimization problem. Then we introduce a risk tolerance parameter for converting this problem into a single-optimization one, which can be solved by the algorithm of group search optimizer. Finally, the effectiveness of the proposed model is verified by simulation studies on a modified IEEE 14-bus power system.

Random Fuzzy Optimization Model for Short-Term Hydropower Scheduling Considering Uncertainty of Power Load

This paper proposes a random fuzzy optimization (RFO) model for short-term hydropower scheduling, in order to avoid frequent unit switches owing to uncertainty of the power load. The cascade hydropower stations of Qing River in China are firstly introduced for research, and the power load error of the cascade is described. Defined as a random fuzzy variable, the error is analyzed and its distribution is derived to signify the uncertainty. Then random fuzzy programming is integrated into short-term hydropower scheduling to develop the RFO model, with more attention payed to unit spare capacity, while satisfying the demand of power grids. Meanwhile, an applicable solution searching strategy is proposed to solve the model. Simulation results of random fuzzy optimization of the cascade prove that the strategy is practical and the developed model is effective to avoid frequent unit switches. The method also can provide available references for other cascade dispatching center and hydropower stations with the same plight.