Pingliang Zeng

Stochastic optimal reactive power dispatch method based on point estimation considering load margin

Conventional optimal reactive power dispatch approaches operate mostly in deterministic form where the power injections are fixed. In practice, however, power injections, especially from intermittent renewable sources, and demand are of uncertainties. To address this problem, in this paper, we develop a load margin constrained stochastic optimal reactive power dispatch (LMC-SORPD) method. We first formulated the considered problem into a chance-constrained programming, which is then solved through genetic algorithm and stochastic power flow based on point estimation. Simulation results on several cases demonstrate that the proposed method is able to prevent the risk of under and over-voltage and increase load margin at a cost of a small but acceptable increase of active power loss. Specified chance-constrained handling techniques are adopted to improve the computational speed. Numerical examples validate the effectiveness of those techniques.

A probabilistic load flow method based on modified Nataf transformation and quasi Monte Carlo simulation

To expose operational risk of large-scale wind power integration system, probability distribution functions (PDFs) of input variables are required to model accurately in probabilistic load flow (PLF) analysis. Unfortunately, PDFs are difficult to obtain in reality. Therefore, a PLF method based on modified Nataf transformation and quasi Monte Carlo simulation is proposed in this paper. This method is able to establish PDF of input variables by their first several orders of moments with the employment of spline reconstruction, then quasi Monte Carlo simulation based on Sobol sequence is adopted to obtain the probability distribution of the output variables. Simulation on IEEE 30 bus system and a real power system demonstrate the validity of the proposed method. The results suggest that the proposed method not only has the advantages of modelling input variables accurately and fast convergence, but also can deal with correlation with convenience.

An on-line generation dispatching algorithm to improve small signal stability

To prevent or suppress the low-frequency oscillation in real time, the modal sensitivity with respect to operating parameters is calculated. It is applied to generate the effective control strategy that can improve the system damping. Initially, Fuzzy ISODATA clustering method is used to obtain the coherent generation group, from which candidate generators for rescheduling are formed and further refined by using participation factors. With the efficient parallel computation of eigen-sensitivity, the relationship between modes of interest and operating parameters can be obtained and used to provide system operators with real-time system operation strategies to improve the system damping. The proposed control strategy satisfies many constraints, such as power output limits and real time operational constraints. The feasibility and applicability of the proposed method has been tested using an actual large scale power system of China with more than 20000 nodes. The time taken to solve this large system is just over 2 minute with parallel computation. The proposed method has been implemented in the on-line dynamic security assessment and early warning system of State Grid Corporation of China (SGCC).

Real-time energy management strategies for microgrids

This paper proposes two real-time energy management strategies from both the perspectives of economy and stability, which could be categorized as optimization method and heuristic method. The optimization method constructs an optimization model, aiming at minimizing the total operational cost in least violation of the schedule, to yield a real-time power dispatch scheme for distributed generators (DGs). The heuristic method dispatches the deviated power from schedule based on economy and feasibility. Both methods are applicable to microgrids in different scenarios. Case studies were conducted to verify the effectiveness of the proposed strategies. The results indicate that the optimization method is efficient to handle complicated situation considering various constraints while the heuristic method can provides a good real-time scheme with low time consumption.

An energy storage system configuration method to stabilize power fluctuation in different operation periods

This paper has developed a practical and economical method for energy storage system (ESS) configuration to smooth the power fluctuation in distribution network feeders. The method firstly uses historical load data and the output of renewable power plant to analyse the power fluctuation in the feeder over the required time period. Then the configuration of ESS system, in terms of rated power and capacity, is calculated and determined to minimise and reduce power fluctuation in the feeder over different time-window and operating period. It is found that the ESS configuration is closely related to the operational period for which the ESS is required to smooth the feed power fluctuation, such as over typical a day or year. Furthermore, it is shown that the requirement of ESS is larger when it is required to smooth the feed power fluctuation over an operation period of one year than a day.

Application of Cuckoo Search algorithm in power network planning

Cuckoo Search algorithm is a new bio-inspired algorithm, which describes cuckoos foraging behavior based on parasitized breeding mechanism of cuckoos egg and Levy flight search principle. Cuckoo Search algorithm can explore solution space as much as possible and jump out the local best flexibility. Combined with truncation and rounding theory, Cuckoo Search algorithm is improved to apply to power network planning, which is introduced to solve power network chance-constrained integer planning model and search the global optimal solution. The effectiveness and feasibility of the improved Cuckoo Search algorithm solving power network planning model are verified by taking 77-bus system as computational examples. Compared with particle swarm optimization algorithm and genetic algorithm, the proposed algorithm has less parameters and more powerful ability of global searching.

A novel point estimate method for probabilistic power flow considering correlated nodal power

With the increasing penetration of wind sources, not only the fluctuation of wind power, but also the correlations among wind farms should be considered in power system analysis. Point estimate method is an effective tool for probabilistic analysis. This paper proposed a novel probabilistic power flow(PPF) algorithm that can tackle dependences among nodal power injections. The proposed PPF algorithm extended three-point estimate method by using Nataf transformation which can deal with multi-variables with incomplete information. The advantage of the algorithm is that the correlation can be precisely taken into account and accurate moments of output variables can be obtained. Accuracy and efficiency of the proposed algorithm has been validated by the comparative tests in a modified IEEE RTS-24 system and a modified IEEE 118-bus system.

N-K Constrained Composite Generation and Transmission Expansion Planning With Interval Load

The uncertainties of the predicted load demand and N-K contingencies are very significant aspects to composite generation and transmission expansion planning (CGTEP). In this paper, multi-contingency constrained CGTEP with load uncertainty was analyzed from stringent mathematical view and formulated as a tri-level optimization model. To effectively solve the tri-level optimization, the entire problem is formulated as two problems using Benders’ decomposition: 1) master problem with expansion planning and 2) the sub-problem with the worst case load shedding. The sub-problem is a bi-level optimization problem which can be solved mathematically using strong duality theory and linearization method. CGTEP with the tri-level optimization can endure the disturbances of interval load and N-K contingencies. A benchmark test system is simulated to validate the effectiveness of the proposed approach. Furthermore, for Bender’s decomposition with many sub-problems of worst load shedding, the numerically comparable results of a special case demonstrate that all sub-problems of composite contingencies must be validated at each iteration even if certain contingency meets the standard of load shedding at the previous iteration.

A particle swarm optimization method for the capacity configuration of wind & hydropower & coal for the energy base delivering to the load center

This paper propose a model to deal with the capacity allocation problems of configuration of wind & hydropower & coal combined transmission from the energy bases to the load centers by high voltage DC. A comprehensively optimal objective function is established considering the investment cost of lines and converter station cost, line power loss cost, operation cost, clean energy curtailment cost and clean energy revenue. Besides, the constraints of power system security and peak load regulating capacity are considered in this model. Due to the complex of the optimal model, particle swarm optimization (PSO) with the merits of probability expression and superposition state is chose to solve the objective function. Finally the west grid with abundant wind & hydropower & coal resources is used as the case study. The results can validate the proposed model and algorithm is feasible and effective.

Reliability assessment of a power system with high penetration of wind and cascade hydropower plant considering wind correlation

This paper presents a method to assess the impact on system reliability of wind integration, considering coordinated operation with cascade hydropower. The seasonal characteristics of river flows and correlation amongst different wind farms are considered. Time sequential Monte Carlo simulation and Latin Hypercube Sampling techniques are used to preserve the characteristics of the time series of the wind speed, water inflows, load, etc. Reliability indices are developed and used to assess, the impact of wind energy production on system reliability. The proposed method is applied to northwest grid of China. Results show the validity of the method.