Reliability assessment in active distribution networks with detailed effects of PV systems

Luo²,

et al. 2019

Processes

With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level. Processes 2019, 7, 955 2 of 22The optimal planning of distributed generation sizing and siting is critical to ensure the operational performance of a distribution network in terms of power quality, voltage stability, reliability and profitability. DG planning problems are often defined as multi-objective and multi-constrained optimization problems. A number of review papers [8][9][10] have surveyed the optimization techniques for optimal DG planning in power distribution networks. The aforementioned review papers mainly focused on the discussion of various computational methods and metaheuristic algorithms.Nowadays, industrial systems, such as papermaking, steelmaking, petrochemical, and power generation, are becoming more and more complex. In such cases, data-driven models based on novel nonlinear signal processing and data analysis techniques may provide an attractive alternative [11]. Therefore, it is paramount but challenging to develop effective techniques in modelling, monitoring, and control for complex industrial systems [12,13].Among many optimization methods, multi-objective heuristic algorithms are the main means to solve multi-objective optimization problems today because they can effectively balance multiple objectives for optimal search [14]. At present, the main objectives of DG planning include the lowest investment cost of DG, the lowest environmental pollution, the optimal voltage quality of the power grid, and the minimum power loss. Partha Kayal et al. aimed to minimize the reactive power loss of the DN, to maximi...

Section: Introductionmentioning

confidence: 99%

Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II

Luo²,

et al. 2019

Processes

“…A micro-grid is a cluster of distributed generators and loads operating under different conditions [5]. The distribution networks, which are connected with embedded generators and active controls, are termed as an active distribution network (ADN) [6]. ADN is a new applicable way to change the traditional grid operation of radial distribution mode.…”

Section: Introductionmentioning

confidence: 99%

Distributed processing based fault location, isolation, and service restoration method for active distribution network

Weng

J. Mod. Power Syst. Clean Energy

Luo

et al. 2015

Active distribution network (ADN) is a solution for power system with interconnection of distributed energy resources (DER), which may change the network operation and power flow of traditional power distribution network. However, in some circumstances the malfunction of protection and feeder automation in distribution network occurs due to the uncertain bidirectional power flow. Therefore, a novel method of fault location, isolation, and service restoration (FLISR) for ADN based on distributed processing is proposed in this paper. The differential-activated algorithm based on synchronous sampling for feeder fault location and isolation is studied, and a framework of fault restoration is established for ADN. Finally, the effectiveness of the proposed algorithm is verified via computer simulation of a case study for active distributed power system.

J. Mod. Power Syst. Clean Energy

“…Compared with traditional distribution system planning [3][4][5][6][7], ADS planning must take effects of DER into consideration, including the variation of power flow [8], node voltage [9,10], even power reliability [11] and steady-state security [12] of the distribution network. The research on ADS planning integrated with the planning of DG has drew lots of researchers' attention since years ago.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive power-supply planning for active distribution system considering cooling, heating and power load balance

Shen

Han

Zhu

et al. 2015

An active distribution system power-supply planning model considering cooling, heating and power load balance is proposed in this paper. A regional energy service company is assumed to be in charge of the investment and operation for the system in the model. The expansion of substations, building up distributed combined cooling, heating and power (CCHP), gas heating boiler (GHB) and air conditioner (AC) are included as investment planning options. In terms of operation, the load scenarios are divided into heating, cooling and transition periods. Also, the extreme load scene is included to assure the power supply reliability of the system. Numerical results demonstrate the effectiveness of the proposed model and illustrate the economic benefits of applying distributed CCHP in regional power supply on investment and operation.