Haozhong Cheng

Optimization of Network Configuration in Large Distribution Systems Using Plant Growth Simulation Algorithm

Optimization of network configuration involving the switch statuses is important for the operation in the distribution system. This paper presents a network configuration optimization approach based on the plant growth simulation algorithm (PGSA), which is specially suited to large-scale distribution systems. An elegant design method of the decision variables, which describes the radial feature of the distribution network and considerably reduces the dimension of the variables in the solved model, is developed. Moreover, a detailed description on switch states further improves the efficiency of calculation. The main advantage of the proposed approach in relation to previously published random algorithms is that it does not require any external parameters such as barrier factors, crossover rate, mutation rate, etc. These parameters are hard to be effectively determined in advance and affect the searching performance of the algorithm. The proposed approach is applied to a 33-bus sample system and a large-scale real system. The best solutions of the two systems, which were published in the technical literature, have been found in shorter time than the existing random algorithms. The numerical results demonstrate well the validity and effectiveness of the proposed approach.

An Iterative LMP Calculation Method Considering Loss Distributions

Generally, the locational marginal price (LMP) is obtained by solving a linear programming formulation. Network losses are considered through the preset loss factors based on historical operation information. This usually brings errors in the calculated loss under different new scenarios. Further, the congestion component of an LMP is dependent on the choice of the reference bus. In this paper, a new iterative LMP calculation method is proposed to overcome the aforementioned two drawbacks associated with the traditional LMP calculations. At each iteration, a linear programming problem for market clearing is solved first. Losses on branches are considered as fictitious nodal demands at their terminal buses. Secondly, the AC power flow is calculated according to the dispatch results. Loss factors and fictitious nodal demands are then updated according to the AC power flow solution, thus removing the need and dependence on presetting loss factors. Another important merit is that LMP and its congestion component obtained from the proposed iterative LMP calculation method are independent of choice of the reference bus. The effectiveness of the proposed method is illustrated on two benchmark systems. The importance of obtaining accurate losses and LMPs is also demonstrated.

Probabilistic Evaluation of Available Load Supply Capability for Distribution System

To describe the impact of uncertainties, such as fluctuation of bus loads and intermittent behavior of renewable generations, on the available load supply capability (ALSC) of distribution system accurately and comprehensively, this paper defines a series of meaningful indices for the probabilistic evaluation of ALSC. An efficient simulation method, Latin hypercube sampling-based Monte Carlo simulation (LHS-MCS), combined with step-varied repeated power flow method is proposed to compute the defined indices. Compared with simple random sampling-based Monte Carlo simulation (SRS-MCS), LHS-MCS is found to be more suitable for the probabilistic evaluation of ALSC. It can achieve more accurate and stable ALSC indices under relatively small sample sizes. The calculation speed of LHS-MCS is comparable with that of SRS-MCS under the same sample sizes, and the required CPU time of LHS-MCS is far less than SRS-MCS under the same calculation accuracy. Case studies carried out on the modified Baran & Wu 33-bus and the modified IEEE 123-bus distribution systems verify the feasibility of the defined indices and high performance of the proposed method.

Flexible method for power network planning using the unascertained number

Abstract This paper presents a novel method of power network planning with uncertainty. The unascertained number (UN) method can take the uncertainty of the nodal information with varying degrees of precision into consideration. A new model is defined and a practical case study is presented using the simplified model of the UN method.

Reactive power optimization by plant growth simulation algorithm

A new approach based on a plant growth simulation algorithm (PGSA) is presented for reactive power optimization. PGSA is a random search algorithm inspired by the growth process of plant phototropism. The objective function for optimization is to minimize the system active power loss. The control variables are generator bus voltages, transformer tap positions and switch-able shunt capacitor banks. The proposed method was applied to Ward-Hale 6-bus system and better optimization results have been obtained as compared to the previous work.

Distribution Network Planning Considering Distributed Generation by Genetic Algorithm Combined with Graph Theory

Abstract Due to requirements for improving energy efficiency and meeting renewable and clean energy targets, an increasing amount of distributed generation is being connected to the distribution network. In order to facilitate increasing levels of distributed generation, the medium-voltage distribution network planning method considering distributed generation connection is proposed in this article. In addition to constraints in the conventional medium-voltage network planning model, constraints related to distributed generation connection, such as short-circuit capacity and short-circuit ratio, are considered in the new planning model. To overcome the problems of low heritability and topological infeasibility of the existing genetic algorithm applied to the distribution network planning, this article develops the efficient genetic algorithm combined with graph theory, which avoids the generation of unfeasible configurations. The efficiency of the proposed planning method is shown using a realistic region distribution network in Shanghai.

A Bi-Level Programming Model for Multistage Transmission Network Expansion Planning in Competitive Electricity Market

A new bi-level programming model of multistage transmission network expansion planning (TNEP) based on double-bidding based pool electricity market is presented in the paper. Open access transmission brings new challenges to transmission planning. From the transmission companys view, the goal of optimal transmission planning scheme should not only bring the optimal transmission profit in the market, but also ensure high operation security and reliability. In this paper, the upper level program considers transmission profit maximization in long-term planning, the lower level program considers the social welfare maximization in short-term operation. Furthermore N-1 security constraints are also considered in the model. A hybrid algorithm integrated with niche genetic algorithm and prime-dual interior method is used to solve the bi-level programming model of the transmission network expansion planning. Test results of the 18-bus system and real world 77-bus system show feasibility and right of the method.

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.

Switch group based Tabu search algorithm for distribution network reconfiguration

Distribution network reconfiguration for loss minimization can is realized by changing the status of sectionalizing switch (close) and tie switch (open) while keeping the network configuration radial. The distribution reconfiguration belongs to multi constraints, high dimensions and none-linear combinatorial optimization problem. These characteristics determine solution space would increase explosively as the increase of switch numbers and infeasible solution would be produced largely in the process of switch combination. In this paper, an efficient searching algorithm is presented for distribution network optimization. This method combines the advantages of Switch group and Tabu searching. Switch group concept is adopted as coding for reducing infeasible solution candidates; Tabu search algorithm is applied for searching the best solution among candidates. At last, the presented method is validated by typical testing system and the comparison with other searching strategies is made to illustrate the effectiveness and reliability.

Transient Instability Detection Using Line Transient Potential Energy

Transient instability detection is investigated using a new analytical tool derived from the line transient potential energy. Instead of following the conventional approach of observing the rotor swings or comparing the critical energy and transient energy at fault clearing time, a new instability detection criterion based on the examination of the line potential energy along post-fault trajectory is developed. The main advantage of this method is that only local dynamic measurements of transmission line variables (power and phase angle differences) are required for detecting first- or multi-swing transient instability. Case studies on the 10 machine 39 bus IEEE test system showed that the proposed method is very effective and opens up new possibilities of studying transient stability and control using local transient network energy.