Wei Zhou’s research while affiliated with Dalian University of Technology and other places

In the deregulated energy market environment, small-scale peer-to-peer (P2P) energy trading can increase the distributed photovoltaic power generation consumption and promote local energy balance. However, it also increases the possibility of security constraints violations during the operation of the utility grid. To solve physical network congestion caused by distributed P2P energy trading, we propose a method that considers the time sequence of P2P energy trading based on a continuous double auction (CDA) mechanism. In addition, to make full use of the users’ flexibility to solve the network congestion, a two-tier market (P2P energy market and ancillary service market) coordination mechanism is proposed. The congestion management model includes two parts. One is congestion responsibility determination and congestion cost allocation in the P2P energy market, and the other is flexible services purchasing in the ancillary service market for minimizing congestion costs. The cost and benefit constraints are specially added in the congestion management model to reduce the economic loss of the grid and users. The proposed method is tested on 11-bus and IEEE 33-bus radial distribution systems to illustrate the effectiveness and scalability of this method. The simulation results show that the proposed mechanism and model effectively perform congestion management considering distributed P2P energy trading, and the results are profitable to users participating in both markets.

Particle swarm optimization (PSO) is a widely used intelligent optimization algorithm. The premature convergence and the constraint handling, however, remain problems for the global optimization searching. In this paper, a dual fitness value chaotic PSO algorithm with equality constraint handling (EDFC-PSO) is proposed. The particle mutation process based on the chaotic Cat mapping is introduced to increase the diversity of particles. Equality constraints are solved by parametric equations, and inequality constraints are considered using the dual fitness value. The proposed EDFC-PSO algorithm is tested by several test functions and then compared with other algorithms. Further, the performance of the proposed algorithm is validated by a combined bidding model of a power system. Our EDFC-PSO algorithm has equality constraints satisfied and enhances the global searching capability in both the test functions and the engineering model.

Combined bidding strategies can reduce the imbalance penalty caused by wind power uncertainty. Power generation stakeholders can benefit from the combined bidding with a suitable coalition. However, the coalition formation method for wind farms with other resources is less reported. In this paper, a sequential coalition formation method is studied. The relationship constraint of wind-thermal combined bidding in a day-ahead market is described by the star graph. Technique for order preference by similarity to ideal solution (TOPSIS) is used to rank the thermal power plants considering multiple evaluation criteria. On the basis of the star graph and the TOPSIS, the coalition structure graph (CSG) is simplified. An actual wind farm and five thermal power plants in northeast China are utilized to verify the effectiveness and practicability of the proposed method. The results show that the proposed method can reduce the computational complexity of coalition formation. When wind power deviations are 0.1, 0.2, 0.3, and 0.4, the retrieval coalition structures are only 4.93%, 4.93%, 6.40%, and 7.39% of those in the basic CSG. The utilities of the formed coalitions have increment rates of 1.30%, 2.79%, 4.13%, and 5.45% compared with those under separated bidding in different wind power scenarios.

Energy sustainable development is of strategic importance across the world. This is especially the case in China as it is the world's largest energy consumer. However, renewable energies such as wind and solar energy are characterized with high intermittence and fluctuation. As the penetration of renewable power in the energy system increases, it is getting more challenging in power dispatch. In this paper, an adaptive time division power dispatch strategy based on numerical characteristics of net loads is proposed. In this strategy, a dispatch time division method is introduced in detail to divide the dispatching time according to the numerical characteristics of net loads. The sample entropy theory is utilized to calculate the complexity of the net loads, depending on which a specific thermal generating mode is provided. The experiment simulation is developed on an actual provincial power system in the northeast of China. The test results have confirmed that in the adaptive time division power dispatch strategy, the ramping power of thermal generators are decreased, the continuous and steady operation time of thermal generators are improved. As a result, this new strategy, can lead to improved energy efficiency of power systems as well as significant environmental benefits.

Driven by high penetration renewable energy roadmaps, making full use of wind power is still an important work for wind industry and the goal of China. Considering the multiple stakeholders in power supply side and the unbalanced development of them, coordinated dispatch can maximize the wind power utilization. Deep peak regulation (DPR) market in China now has facilitated the coordination. However, wind farms in the market are involved passively for the cost sharing which is not conducive to wind farms to make full play of it advantages. Besides, the electricity of wind farm benefiting from deep peak regulation market cannot clearly calculated. To solve this problem, a bilateral coordinated dispatch of wind power and other power generation companies (GENCOs) can be introduced in deep peak regulation. In this paper, the bilateral coordination of wind power and other GENCOs in DPR is studied. The potential economic feasible regions in bilateral coordination are analyzed and the compensation pricing ranges are given according to the individual rationality (IR) constraints. The contributions of different participants is clearly calculated using Shapley value, according to which the fair profit allocation is ensured. An affine decision rule is utilized for the simplification and fast computation. A real system in northeast China is utilized for case study to verify the potential economic feasible region. The results can provide reference for the implement and profit allocation of bilateral coordination.

The integrated operation of wind storage is a developmental trend for future wind power stations. Compared with energy storage and wind power system scheduling, the utilization ratio of wind power is improved. This paper analyzes the power system scheduling risks that are brought about by wind power stations with wind and energy storage integration and puts forward the corresponding risks indexes, which are based on the physical structure and the long-operation features of the battery energy storage system. This paper also proposes the multi-objective optimization scheduling model, considering the economy of optimization, risk of load-shedding, and wind power curtailment that is caused by the full failure and partial failure of energy storage and wind turbines, and the uncertainty of the wind power output. Example results have shown that the optimization scheduling model can apply to various control strategies and different risk levels of the system, and reduce the risk of an electric power system containing a wind power station with wind and energy storage integration. In the meantime, it can also improve the economical efficiency and utilization rate of the wind power system.