Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation

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.

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.

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.

An energy storage device is a multi-physic device with ability to store energy in different forms. Energy in electrical systems, so-called ?>electrical energy ?>, can be stored directly or indirectly, depending on the means of the storage medium. Devices that store the electrical energy without conversion from electrical to another form of energy are called direct electrical energy storage devices. Two major energy storage devices are ultra-capacitor energy storage (UCES) and super-conducting magnetic energy storage (SMES). Devices that convert and store the electrical energy in another form of energy are called indirect electrical energy storage devices. Electro-mechanical storage devices are flywheels, compressed air energy storage (CAES) and hydro pumped energy storage (HPES). Electrochemical energy storage devices are electrochemical batteries and fuel cells. Above mentioned energy storage technologies and devices are briefly described in this chapter. Different fields of application are also discussed.

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.

Technical and economic impacts of active management on distribution network

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.

Robust MPC-based microgrid scheduling for resilience enhancement of distribution system

The optimal location and capacity of distributed generation (DG) is essential for electric power systems especially to satisfy the load demand. This paper proposed the hybrid optimization method to obtain optimal location and capacity of DG considering energy storage in dynamic systems. Artificial bee colony (ABC) algorithm and quadratic programming (QP) is applied. The objective functions of the proposed method minimize the cost of distribution line losses and load shedding. ABC algorithm is used to find the optimal location and capacity of DG. Then, objective functions are calculated using QP with dynamic condition of the load profile for 24 hours. Load shedding is generated based on power generation which can’t satisfy the load demand. The proposed method has been implemented in a modified IEEE 30 bus.

With the proliferation of renewable energy sources and the elevation of environmental concerns, it is expected that microgrids will become one of the major means for residential energy supply. However, the distributed nature of microgrid operation brings new technical challenges to energy management. Endowing wireless communication capability to the distributed generation (DG) units and energy storage (ES) devices in a microgrid is beneficial for their cooperation without a centralized controller. Yet, how to establish distributed energy management without \emph{a priori} statistical information for all the DG units and loads still requires extensive research. In this paper, a reinforcement learning algorithm with cooperative neural fitting iteration is proposed for distributed energy management in microgrids via wireless networks. The reinforcement learning algorithm leverages a distributed actor- critic structure to adopt the continuous states and action spaces of a microgrid. A diffusion strategy is incorporated in the reinforcement learning algorithm to coordinate the actions of DG units and ES devices by exchanging their evaluations and decisions via a wireless network. Simulation results based on realistic renewable power generation and load data are presented to evaluate the performance of the proposed algorithm.

The reasonable regulation strategy of distributed generation (DG), which is a key link in the operation of distribution network (DN), can improve the penetration of new energy, reduce voltage fluctuations and network losses effectively. Therefore, the control model of DG should be fully considered in the planning stage to maximize its complementary ability. In this paper, a bi-level distribution network planning model with DG control model is established, aiming at economy and stability. In the upper level, the capacity and position of DG are determined with the minimum annual comprehensive cost as the objective. The lower level calculates the DG control strategy when the voltage deviation is minimized, and uses particle swarm optimization to solve the model hierarchically to obtain the DG installation position and capacity with the optimal annual cost and voltage fluctuation. Case study on IEEE 33-bus system verifies that the model can improve the collaborative control capability of source and network effectively and make DG regulation work.

Due to liberalization in the power market stake of the Distributed Generation (DG) in the power industries has increased radically. Integration of DG will result is the change in the operating conditions of the existing power system network. Due to this DG has drawn attention of utility providers, policy makers and, to effectively use the DG, several researchers also. Inclusion of DG in the existing power system may enhance its power transfer capacity, voltage profile, reliability and it can also reduce the overall system losses if installed in proper capacity and at proper place. Benefits of DG can be efficiently extracted only if an appropriate capacity of DG is introduced in the existing power system at appropriate place. This chapter proposes an analytical and heuristic approach suggesting the optimum size and location of type-1 and type-2 DG. The proposed method is implemented on the IEEE-13 bus radial distribution network (RDN) and result shows the validity of the proposed method.

This paper meticulously evaluates the reliability of distribution networks integrated with distributed generation (DG), considering various user classifications. Initially, we delve into the daily load profiles of residential, commercial, and industrial sectors, establishing a nuanced time-series load model that encapsulates the distinct characteristics of these customer segments. Subsequently, we formulate a model for the harmonious operation of energy storage systems, tailored to the interplay between DG output and load requirements. Utilizing the sequential Monte Carlo simulation algorithm, we compute the reliability metrics for the IEEE RBTSBUS6 feeder system, revealing that DG integration significantly enhances distribution network reliability. Notably, identical DG output yields varied impacts on the reliability of distinct customer sectors, underscoring the need for tailored strategies in DG deployment.

Configuration, operation and management of existing distribution networks are based on passive networks, whereby the role of such networks is confined to transferring electricity from generation and transmission system to load centres. Under this situation, the main function of distribution network is delivering good quality electricity with minimum amount of network control to the load centres at different voltage level regardless of the most critical case. Most distribution networks are designed to operate in radial mode without having generation unit accommodate on them. Continued increase of distributed generation (DG) penetration into existing distribution networks in recent years has resulted in bi-directional power flow and changes in network voltage profile. These in turn caused important impact on the operation of conventional feeder protection schemes that are originally designed based on radial network operation assumption. The protection issues that had been identified in the presence of DG include (i) malfunction of protective devices due to increase of network fault current level, (ii) unnecessary tripping of non- directional relays due to significant fault contribution by DG, (iii) reduction of fault detection sensitivity of overcurrent relays due to fault contribution of DG, (iv) fault clearing time of graded overcurrent relays is affected and (v) operation of re-closer is affected with introduction of LOM relay. It has been reported that future distribution networks are likely to be operated in ring mode rather than radial in order to optimise the integrated capacity of DG. There are advantages in adopting ring mode operation but it could complicate the protection systems of networks with integrated DG. This paper reports an investigation into the impact of distributed generation on the setting of protective devices of distribution networks that originally operated in radial mode and then switched to ring mode and to compare the protection requirements for the two modes of operations.

This paper presents a novel algorithm for representing the effects of an optimal configuration of distribution energy storage system (DESS). The method was developed in the context of a framework in power distribution network with DESS, which includes such considerations as: SOC, cost of energy storage device, electricity price, transmission losses. The presented method provides a means for computing an optimal schedule of DESS energy resource allocation for new energy sources and loads power fluctuation, which can synthesize all useful energy storage devices, that is better for system stability and is contributed to the interests of energy storage owners, because energy storage device usually cannot be used adequately. Of particular interest, the method considers the relation between the depth of discharge and the use-cost of different energy storage device and the influences of transmission losses.