Multi-Objective Bilevel Coordinated Planning of Distributed Generation and Distribution Network Frame Based on Multiscenario Technique Considering Timing Characteristics

Zhang

Energy Science & Engineering

et al. 2019

To efficiently reduce the fault currents of active distribution networks (ADNs), this paper proposes a novel methodology for Pareto optimal allocation of resistive‐type fault current limiters (R‐FCLs). The proposed approach enables to simultaneously optimize the number, location, and size of the R‐FCLs in the ADNs with inverter‐interfaced and synchronous distributed generators (DGs). The sensitivity analysis is introduced to rank candidate locations, and a constrained multiobjective function is created to minimize the cost of the R‐FCLs and the fault currents of the ADNs. An improved multiobjective particle swarm optimization (MOPSO) algorithm and a multiobjective artificial bee colony (MOABC) algorithm are designed to obtain the Pareto optimal solution set. The proposed approach is verified using the modified IEEE 33‐node and 69‐node distribution systems, where both centralized and dispersed access of DGs are considered. The numerical results demonstrate that: (a) The fault currents in all nodes of the two testing systems are limited to the permissible levels using the least capital cost of the R‐FCLs, and (b) the improved MOPSO outperforms the MOABC to achieve a better Pareto front and decrease the number of iterative computation. In consequence, the feasibility and superiority of the proposed approach are well validated.

Section: Performance Verification Of the Proposed Approachmentioning

confidence: 99%

Pareto optimal allocation of resistive‐type fault current limiters in active distribution networks with inverter‐interfaced and synchronous distributed generators

Zhang

Energy Science & Engineering

et al. 2019

IEEE Trans. Power Syst.

“…wind and solar power generations, load growth, asset availability, future capital costs, etc. The models and solution methodologies developed to address uncertainties can be classified into the following broad categories: a) Monte Carlo simulation nested within a single-or multi-objective optimization model [53][54][55][56][57][58]; b) Probabilistic-stochastic models incorporated into optimization routines [59][60][61][62][63][64][65][66][67]; c) Stochastic programming models [68][69]; and d) "Scenario-like" models integrated within optimization models [70][71][72][73][74]. The most important drawbacks are: a) Distribution planning in utilities is always based on deterministic criteria and there is no simple way to link them with the probabilistic results; b) Results interpretation can be very difficult as well as subsequent decision making using these results; and c) Large amount of data may be required.…”

Section: Probabilistic Decision Treesmentioning

confidence: 99%

“…The first two downsides are the biggest obstacle in applying any such method in real-life, because design engineers would never propose a solution they don't understand and/or is not compliant with the planning standards. In that context, the last group of methods [70][71][72][73][74] is best suited for real-life planning; however, all methods produce amalgamated solutions for all considered scenarios in the horizon year. This can be deemed as an advantage in the academic world because the global optimum is obtained; however, in real-life, each investment decision needs to be clearly justified to the Regulator and amalgamated solutions are not welcome.…”

Section: Probabilistic Decision Treesmentioning

confidence: 99%

Operational Planning of Distribution Networks Based on Utility Planning Concepts

Mansor

Leví

2019

The paper presents the operational planning of medium voltage (11 kV; 6.6 kV) distribution networks that is an integral part of the previously developed integrated planning approach based on utility planning concepts. The operational planning problem is decoupled from the investment planning stage and it is further divided into two phases, the quality of supply (QoS) planning and the minimization of operational costs. The QoS planning maximizes the benefits of the regulatory incentive regime by installing and automating switchgear, as well as finding optimal normally open points (NOPs). It is solved in two ways, by applying genetic algorithm (GA) optimization and then the approach based on engineering rules. Operational cost minimization considers costs of O&M, switching, losses and reliability. It includes security constraints for radially operated networks and allows that the locations of new switchgear from the QoS stage are changed. The simplified version of the model is also intended to be used as a real-time network reconfiguration tool. The entire methodology is tested on the 69-bus test system.

“…The objective function of [10] aims to minimize distributed generation (DG) installation cost and network losses. In [11], a DG planning scheme is proposed to minimize the network upgrade cost, network loss cost, power interruption cost, and users' power supply cost. A compromised scheme is obtained in the non-inferior solutions.…”

Section: Introductionmentioning

confidence: 99%

Planning of High Renewable-Penetrated Distribution Systems Considering Complementarity and Cluster Partitioning

Ding

Sun

et al. 2019

Energies

Photovoltaic (PV) and wind power (WT) resources can influence each other in some scenarios, and this influence tends to show that the rise of PV resources may indicate the drop of WT resources, and vice versa. This pattern of PV and WT resources influencing each other is called the complementary characteristics of PV and WT power. The complementary characteristics of the power outputs of different kinds of distributed renewable energy resources (DRERs) and the correlation between DRERs outputs and loads can impact the consumption of DRERs by the loads within the grid, which represents the rate of DRER outputs consumed by loads instead of being reduced. In this regard, this paper investigates a planning strategy for DRERs considering these two factors. An improved co-variance matrix method is applied to generate complementary samples of DRERs and correlated samples of DRERs and loads. The samples generated are used to study the impacts of the degree of correlation between DRERs and loads on the consumption ability of DRERs. The concept of the cluster is introduced as a region including DRERs with complementary characteristics. Based on the cluster partition method and the samples generated, the DRERs planning model is proposed to maximize the profits of different DRER stakeholders. The planning model is transformed into a single objective model through the ideal point method. A Benders decomposition-based method is developed to efficiently solve the proposed model, and an actual network in China is used to illustrate its performance. The results show DRER consumption can be significantly improved by the proposed planning model.