The Coordinated Planning of Distributed Generation and Distribution Network Considering DG Control Models

Technical and economic impacts of active management on distribution network

In order to solve the low-voltage problem of three-phase four-wire system in the low-voltage distribution network by using distributed generation (DG), this paper establishes the mathematical model of the DG configuration, and presents the DG placing method and configuration principle, considering the influence of unbalanced load, reactive power compensation and the existing DG of the network. For the phase voltage deviation and the phase power flow calculation, the mathematical model and the calculation method are given in this paper. According to the voltage deviation and the power flow under the maximum load condition, the placing site of DG is determined. Based on the expectation of the node voltage deviation percentage wanted to improved, the maximum current and maximum capacity of DG are deduced. In order to ensure the normal operation of the low-voltage distribution network, the voltage distributions are checked under the minimum load after conFigure the DG, and the minimum injection node current and minimum capacity of DG are determined in the case of minimum load. Taking the practical 0. 4kV three-phase four-wire network as an example, the DGs are configured to solve the low voltage problem. By the analysis and comparison of voltage distribution, the proposed placing DG method can be applied to different load cases, and it is an effective method to solve the low-voltage problem.

Coordinated Optimal Allocation of DGs, Capacitor Banks and SOPs in Active Distribution Network Considering Dispatching Results Through Bi-level Programming

With recent development of technology and management in power market and equipment, more and more distributed generation (DG) is embedded in the distribution network. However the approach of connecting DG in most cases is based on a so-called ‘fit and forget’ policy and the capacity of DG is limited rigidly by distribution system operator to avoid the negative effects of high level penetration. New management technologies have been proposed to handle the integration of DGs in the distribution networks. In this review, the micro grid (MG) was treated as the local control method to coordinate DGs within a small area of distribution network. And the active distribution network (AND) was treated as the global control mechanism to actively manage DGs, MGs and other equipment. The operation framework of ADN was firstly introduced. Then based on the static and dynamic models of DGs and MGs, impacts of DGs and MGs on the ADN are surveyed from power quality, stability to the operation. Finally, the conclusion and suggestion is given in this paper.

Network Interconnection Studies of Distributed Generation

Distributed generations (DGs) are widely used to meet electric energy demand in distribution networks. DGs are advantageous compared to the centralized generating units in many ways including reducing power loss, improving voltage profile, enhancing network stability, decreasing pollution, releasing transmission line capacity, and reducing congestion of distribution lines. The size and site of DGs logically influence power loss in distribution networks. The optimal place and capacity of DGs are determined aiming at minimizing power loss in distribution networks. In this article, firefly algorithm (FA) is used for optimum placement of various DGs in distribution networks. Optimum power factor of DGs along with active and reactive powers are obtained in this work. Various DGs of generating/consuming types are considered in the suggested method. Performance analysis of the proposed method has been done on standard IEEE 33 bus system in MATLAB.

This paper presents an approach to coordinated planning of distribution network (DN) and distributed generation (DG) considering uncertainties of output of DG and load. To overcome shortage of traditional separated planning of DN with DG and maximize positive influence brought by DGs, the multi-objective integrated planning model of DN and DG is established, in which the types, location, capacity of DGs, lines in need of being upgraded and lines to be built for access of new load point are taken into account as variables, and investment, power purchasing, network loss and power failure are considered as objectives. In face of uncertainties of load and output of DGs, first the corresponding probability models are established, then latin hypercube sampling (LHS) based on those is adopted to get samples and research scenarios are obtained through samples reduction. A multi-objective chaos improved PSO based on theory of pareto optimal solution set is proposed to solve the model. Finally, the method is applied in improved IEEE 33-node distribution system and effectiveness and feasibility of the model and algorithm are verified.

The advent of distributed generation (DG) units has posed many problems to the planning and operation of distribution networks (DS). The selection of the “best” site for the DG installation is a major problem in this context. Different strategies and methods have been developed in the recent years which deal with this problem. However, Complexity of solutions and inadequate manipulation have hindered their utilization by DS planners. This paper presents a robust and practical tool which helps the distribution network planner in getting the best possible location of DG unit taking into account both reliability of supply and voltage sag problems. The method suggested uses knowledge-base expert system (ES) to help in selecting the appropriate locations. The method suggested is based on economic and technical considerations. It investigates with economic loss due to both voltage sag and forced outage of DS components. It concerns with the effect of location of the added DG on the overall network performance during both steady-state when a forced outage occurring on one or more component of the DS and transients after sudden short-circuit. Using ES avoids mathematical complications of solving complex analytical model. By judicious rule-base ES, selection of optimum locations becomes clear and simple. The suggested method is explained and implemented through practical distribution network and its effectiveness is tested and discussed.

For the past few years, to reduce system power losses and maintain operating constraints, such as voltage stability, network reconfiguration has been crucial in determining the radial operating framework. Distributed generation (DG) is typically used to generate energy close to the site of consumption. This technology generates energy that is affordable, in contrast to conventional energy production. To lessen energy losses as well as boost voltage characteristics, the adopted methodology is centered on reconfiguration and DG distribution in the radial distribution network (RDN). In this work, the loss sensitivity factor (LSF) is used to determine the right DG position in RDN. After identifying the bus for DG positioning, the artificial rabbits optimization (ARO) technique is used to ascertain the ideal reconfigured network and DG size to lessen energy losses and enhance the voltage profile for RDN. The employed methodology is investigated on IEEE-33 and 69 RDN, respectively, for two cases of considering only reconfiguration without distributing units of DG and reconfiguration with an allotment of three DG units. The latter case showed better results compared to the case of only reconfiguration.

When the large amount of Distributed Generations (DGs) are connected to the grid, the whole distributed power network lost line, and the voltage fluctuates are obvious. In recent years, the concepts of power Internet of Things (IoT)technology and energy Internet are proposed. Therefore, power grid technology has entered a new stage. The IoT technology is characterized by high bandwidth, high transmission rate, and low latency. In addition, with the collected data are increased, IoT becomes an effective way to improve the computing power of edge nodes in edge computing. In order to reduce network losses and voltage fluctuations, reactive power optimization is need to be implement in distribution network system. This paper proposes a distributed power voltage regulation strategy based on the particle swarm optimization and deep belief network (PSO-DBN) under the IoT. Firstly, IoT technology and characteristics are introduced, and the current research status of voltage regulation technology are analyzed in distribution network. Secondly, the weight of DBN is improved with PSO. Finally, the IEEE33 bus system is taken as a simulation case, the effects of daily reactive power optimization and the amount of historical data is analyzed. It is concluded that the method proposed in this paper has obvious effects on reducing voltage fluctuation and network loss. KeywordsDistributed generationIoTPSO-DBNReactive power optimization

Distributed generators (DGs) are nowadays most commonly used in distribution networks to meet the rising demand. The distribution network power flow is altered as more distributed generations are integrated with the distribution network. Analyzing and researching the network's power flow with distributed generation is an essential element of analyzing the distributed generation's influence on the networks. This study proposed new sites in the test network for the purpose of connecting DGs. Good results were obtained in improving the necessary network elements. Firstly, applying a Newton-Raphson method to measure the distribution network's power flow without distributed generations. Then the optimal power flow approach with DGs is set up based on many goals such as minimizing fuel cost, minimizing losses, voltage deviation, increasing efficiency, and the incremental fuel cost in the network appeared. Eventually, applying Particle Swarm to overcome the optimization problems. In MATLAB software environment and programing MATPOWER version 6.0, the simulation is evaluated and validated using the IEEE30 node method. The test results indicate that optimal power flow analysis with distributed generation will accomplish many networks aims and boost system economy.

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW.

Distribution network reconfiguration problem has been known as a common issue in distribution network operation. Up to now, distribution network reconfiguration has been used for minimizing the power loss, reliability concerns and emergency problems. According to the increasing installation rate of Distributed Generations (DGs) and the rapidly growing number of Electric Vehicles (EVs), as a large responsive loads, distribution network operators have encountered new problems. These new aspects of network operation problem consist of congestion problem and require to established distribution market to execute the market process. This paper presents a novel methodology to solve this problem with the application of Distribution Locational Marginal Price (DLMP), using Genetic Algorithm (GA). The standard IEEE 33-bus network has been selected as a suitable case study and simulation results demonstrate the compatibility of proposed method. Comparison with other papers have also been addressed in case of not considering DGs and congestion issue. In the following, DGs participation in distribution electricity market, the congestion problem and numerical results has been shown and discussed.

With the increase of distributed generation(DG) in distribution networks, the impact of these DGs in the power grid is also increasingly significant. In particular, with the development of intermittent DG which is more difficult to predict power generation, it challenges the reliability of the power grid. According to the characteristics of the grid with DG, this paper simplifies the distribution network to a system which takes autonomous control units(ACU) as the main component. Then the power grid is divided and classified into autonomous control regions(ACR) and calculates the reliability indices of different classes of ACR. And through the reliability index to establish an optimization model, eventually configure the system's DG capacity and improving the system's reliability.

The transition from fossil fuel-based generation toward renewable-based distributed generation (DG) has motivated system operators to determine the maximum DG capacity that can be safely accommodated in distribution systems. Active network management (ANM) schemes can mitigate the technical issues arising from high DG penetration and, therefore, increase DG capacity in a distribution network. This paper develops an optimal power flow (OPF)-based DG capacity assessment approach that respects the local standard ANM schemes, particularly to control the grid-connected inverters. First, we investigate the effectiveness of different standard local ANM schemes on improving the DG capacity of distribution systems. We then compare them with the central approach that assumes a live, two-way communication infrastructure as an upper limit benchmark for the DG capacity. We use the proposed model to obtain the DG capacity of the three-phase IEEE 37-node and IEEE 906-node networks in the presence of different local and central ANM schemes. Our simulations show that real power based local control strategies are more effective than their reactive power based counterparts in improving the DG capacity of both networks. Additionally, in the voltage-constrained test system IEEE 906-node, the central approach enhances the network's DG capacity at least 25 % more than local approaches.