A non-simulation-based linear model for analytical reliability evaluation of radial distribution systems considering renewable DGs

Future smart electrical distribution systems could adopt switch remote control and advanced automation systems to operate in autonomous mode (islanding) some portions of the network in order to improve system reliability. In this perspective, a free code able to assess distribution reliability by using analytical or Monte Carlo simulation methods accounting for intentional islanding operation is presented. An effective network representation and data structure, which have led up to the implementation of two automatic and general procedures, are proposed. The procedures share common underlying information in order to speed up calculation. A further merit of the tool is that it enables the user to quickly and simply create the input files. The reliability indices of three distribution networks have been computed by means of the developed software for different scenarios, that is when island mode of operation is not permitted by regulation, and when island mode is permitted (intentional islanding) with the local load either partially or totally supplied by renewable distributed generators. The free code, the input files and the results related to the considered networks are available online at: http://www.dieei.unict.it/users/sconti/psrs.htm.

Interest in distributed generation (DG) arose in the 1990s with the deregulation of the electricity industry and the development of improved DG technologies. Furthermore, the appearance of high-technology loads requires an increase in the reliability of the power system. In this new scenario, it is important to consider DG in the planning of distribution networks. For example, a utility can install DG to provide additional capacity to feeders or substations. Additionally, DG can be used to improve the restoration capability of the distribution networks, that is, DG can eliminate network constraints (voltage drop or feeder loading) during the restoration process. Consequently, DG can improve the reliability indices associated with the restoration time. In this paper, a methodology to estimate the impact of DG on the reliability indices of distribution networks is proposed. This impact is assessed considering that DG can be connected to an isolated feeder (isolated operation) or to a feeder with tie-lines (interconnected operation). The network constraints model used in this paper is based on a simplified version of the power summation load flow method and compensation techniques. The results with the proposed methodologies, to model the isolated and interconnected operation of DG, demonstrate that DG has a significant impact on the reliability indices of distribution networks. The proposed methodologies have been validated and tested in a large distribution test system. The results show the importance of considering the impact of DG on the reliability indices of distribution networks

With the setting up of regulatory authorities the cost pressure on the electricity distribution companies will increase further in Germany. Due to political reasons the distributed generation (DG) has increased. Therefore, nowadays many distribution network companies face two important tasks: reduction of network costs to improve the network efficiency and integration of the large-scale DG into existing systems. A long-term planning of the distribution networks can provide an optimal target network for the efficiency analysis of distribution networks and the evaluation of effects, which are caused by the DG. Planning of distribution networks without the DG is known as a non-linear mixed integer problem. Additional technical constraints (such as voltage rising effect, short-circuit current, etc.) and stochastic characteristics of the DG make the planning problem become more complex. In this paper we propose a new efficient two-stage heuristic method, which is based on large neighborhood search and guided local search, for the planning problem of distribution networks with the DG. Technical modeling and stochastic characteristics of the DG are described. Applications of the proposed method for the planning of real German distribution networks with large-scale DG show that distribution network companies can use this method, not only for planning the future optimal network structure but also for analyzing the effects, which are caused by different DG scenarios

Network Interconnection Studies of Distributed Generation

Due to intermittent renewable energy and fluctuating load demand, distribution networks with renewable distributed generation (DG) installations are more likely to suffer voltage issues and significant power losses. The performance of conservation voltage regulation (CVR) schemes may be adversely affected by the undesirable voltage profile at specific nodes. This paper aims to reduce power losses in CVR‐implemented networks by optimally planning new renewable DGs without changing the existing ones. A scenario‐based optimal renewable DG planning model is proposed with a novel scenario formation method. The uncertainties of load demand and renewables are captured jointly and formed into a finite number of scenarios based on a multivariate Gaussian mixture model (MultiGMM). The locations and capacities of different types of new renewable DGs are optimally planned for CVR performance improvements on power loss saving by aggregating the operation status and probabilities of the scenarios using mixed‐integer non‐linear programming (MINLP). A time‐series simulation is formulated for accuracy verification. The results of case studies show that the proposed model can significantly reduce power losses, active load demand, and reactive load demand. The accuracy of the planning results can be guaranteed with fewer scenarios compared to a widely used classical scenario‐based planning method.

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.

Reliability correlated optimal planning of distribution network with distributed generation

A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

Design and analysis of electrical energy storage demonstration projects on UK distribution networks

Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.

Distributed generation (DG), which is interlinked with distribution system, will inevitably have an impact to system. In the paper, a reliability evaluation method is proposed, which is based on the analysis to the structure and power flow of distribution system with distributed generation. First, a new concept, the smallest unit, is put forward for improving the fault traversal networks times. The authors use the depth-first search in data structure to divide distribution system into several the smallest units, and propose the islanding operation mode to increase the reliability of the system. Distribution system will be automatically turned into the islanding operation mode once faults occur. To evaluate quantitatively the reliability of distribution system, Monte Carlo simulation method is used by simulating the running status of distribution system. At last, the calculation results of actual case show that the proposed method is available to evaluate the reliability of distribution system with distributed generation.

the penetration of renewables among residential customers has been increasing over the last decade. In this regards, the operation of both market and distribution network has faced many challenges. In this study, the impact of customers' engagement in terms of peer-to-peer (P2P) energy trading on short-term planning and operation of the distribution network is investigated. Furthermore, the optimum investment plan for customers is included in the proposed model. Simulation case studies are carried out to verify the impact of the P2P market on distribution network planning.

This paper proposes a multi-agent control scheme in order to coordinate between distributed battery energy storage systems (BESS) and renewable distributed generation (DG) units in distribution networks. The proposed control scheme aims to utilize BESSs as distributed voltage controllers across the distribution network, in such a way to overcome the intermittent high-ramp rate of renewable DG units. To that end, the voltage control in distribution networks is formulated as a distributed constraint satisfaction problem (DisCSP). An asynchronous weak commitment approach is utilized to solve the formulated problem. Several case studies are carried out to test the effectiveness and performance of the proposed algorithm under different operating conditions.

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.

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.