Optimal planning of distributed generation from the DisCo and the DGO viewpoints considering the uncertainties in future demand and electricity price

Since electricity demand is increasing continuously, it is necessary to invest in expansion of distribution network capacity. From the asset management point of view, it is necessary to encourage the private sector to invest in distribution network. To do this, distribution network managers must provide important opportunities for private sector to profit from their investments. One of the options for private sector is to invest on distributed generations (DGs). In this regard, distribution company (DisCo) must sign power purchase agreement (PPA) with DG owners (DGOs). So, optimal siting, sizing and PPA rates from economic point of view are important challenges which are considered as the main contribution of this paper. The proposed methodology of this paper applies load and price uncertainties into the planning problem. The proposed scheme involves using non-dominated sorting genetic algorithms II (NSGA II), since it attains non-dominated solutions in which DisCo and the DGO can put their personal preferences into practice. To evaluate the effectiveness of the suggested method, the computer simulations are done on a 33-bus distribution network and the results are discussed.     

Simultaneous placement of renewable DGs and protective devices for improving the loss,reliability and economic indices of distribution system with nonlinear load model

ABSTRACT

In this paper, the simultaneous placement of renewable distributed generation (DG) and protective devices are discussed in the distribution system with nonlinear load model. The load model is considered as a combination of various customers’ daily load patterns and sensitive to voltage frequency. The considered DGs are wind turbine and photovoltaic. The breaker, relay and fuse are considered as protective devices. The objective functions of the problem consist of the active and reactive power loss, the reliability index and the profit of company of distribution system. The whale optimisation algorithm is used for multi-objective optimisation. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions as the best compromise one. The proposed algorithm is implemented on the 69-bus distribution system and actual 101-bus distribution system in Khoy–Iran. The results indicate the high performance of the proposed method in improving the technical and economic indices of the network.     

Evaluation of maximum allowable capacity of distributed generations connected to a distribution grid by dual genetic algorithm

This paper proposes a dual genetic algorithm based approach to evaluate the maximum allowable capacity of distributed generations (DGs) connected to a distribution grid. The uncertainties in the existing deterministic approaches for evaluating the steady-state voltage deviation due to distributed generation are discussed as well. Nowadays, deterministic approaches are widely adopted by those who propose the interconnection of DGs. However, the existing deterministic approaches overlook some operation conditions that may give rise to an incorrect result and lead to a wrong decision in practical applications. In this paper, various factors affecting steady-state voltage deviation are discussed first. Then, a maximum allowable DG capacity evaluation approach based on the dual genetic algorithm is proposed. Finally, the uncertainties of the existing deterministic approaches are discussed. It is intended as reference for utility engineers processing DG interconnection applications.     

Determining the guaranteed energy purchase price for Distributed Generation in electricity distribution networks

With widespread installation of Distributed Generation (DG) in distribution networks and considering their impact on the network, distribution companies (DisCos) have the option of supplying loads from these resources as well as the wholesale market. In the presence of DG, an incentive scheme is required to determine the reasonable purchasing price of energy from DG owners based on benefits to the DisCos. These prices should be determined in such a way that the risk to the DisCo can be controlled. Considering the guaranteed energy purchase price (GEPP) of DG as a risk-management option, this paper addresses the GEPP for a specified future period. The proposed methodology determines the GEPP based on expected loss reduction and reliability improvement achieved by DG. Due to uncertainties associated with load, market price, and future investment, Monte-Carlo simulation is used to determine the GEPP. The performance of the proposed methodology is evaluated for a 33-bus distribution test system and results are discussed for different cases. The obtained GEPP guides investors and planners toward an optimum place and size for installation of DG, which leads to maximum network benefits as well as profits.     

Multi-objective modelling of day ahead scheduling of MMG-based distribution networks accompanied by DEA considering economics,emissions and power quality

ABSTRACT

This paper presents the state of the art relating to multi-objective modelling for day ahead scheduling of multi micro grid-based distribution networks, using optimal power flow (OPF) accompanied by data envelopment analysis (DEA). In this paper eco-reliability cost function, power quality enhancement and emission reduction are treated as the objective functions and the uncertainties of renewable distributed generations (DGs), load demand and market price are incorporated into the problem. This method is able to find the optimum operation of DGs in grid-connected or isolated MGs, power transaction between each MG and upstream networks/other MGs and hourly reconfiguration instants. For this purpose, firstly OPF is applied to the problem, then the obtained optimal solutions are prioritised by DEA and ranking is done, based on the efficiencies of the optimal solutions. Finally, the provided results validate the practicability of the proposed method and accuracy of the outcomes.     

Optimal placing and sizing of parking lots including different levels of charging stations in electric distribution networks

Background: Low emission, high efficiency, and convenience of using plug-in electric vehicles (PEVs) are considered as significant benefits of PEVs. However, installation of parking lots (PLs) in order to supply PEVs causes some financial and technical challenges for electric distribution networks. The changes in system reliability, power loss, voltage drop, and costs associated with the installation of PLs are considered as some of the aforementioned challenges. Therefore, optimal placing and sizing of PEV PLs including different levels of charging stations ((CS); slow, medium, and fast) are presented in this paper. Since PLs have the potential to exchange electricity by electric network, they can be taken into account as distributed generations (DGs) and their installation can be considered from the perspective of installation of DGs. An objective function including system reliability, power loss, voltage drop, and PL cost/revenue is proposed for the optimal planning. Results: Genetic algorithm is employed to solve the optimisation problem. Simulation is carried out on a 33-bus radial distribution network. For the planning purposes of PLs, three different levels of CSs (slow, medium, and fast) in PLs are considered. The effect of increasing the penetration of PEVs in PLs is also examined on planning the PLs. The effect of different dispatch times on the selection of different CS levels (slow, medium, and fast) is investigated as well. Furthermore, the effect of applying tariffs and incentives for the customers is analysed for the selection of CS levels. Finally, the effect and importance of combinations of the CSs with different levels are investigated.     

Cost–benefit analysis for optimal distributed generation placement in distribution systems

This paper presents an optimisation method to determine optimal allocations of distributed generation (DGs) and capacitors based on maximisation of a profit/worth analysis approach. The optimal locations and sizes of DGs and capacitors have been determined by minimising the power distribution loss. This method considers various technical and economic factors such as line losses, sizes of DGs and capacitors at optimal locations, investment costs, operating costs and maintenance costs of DG and capacitor to achieve the objective for a predetermined number. The electricity market price of grid power has been considered to recover initial investment in a specified time period. The improvement in the voltage profile of the system has also been considered in this work. The particle swarm optimisation technique has been used to solve the optimal placement of DGs and capacitors to maximise the profit. The proposed technique is tested on 33-bus and 69-bus test systems.     

Hybrid approach for congestion management using optimal placement of distributed generator

Congestion management (CM) in a large power system network is a difficult task which can be solved by placing one or more distributed generators (DGs) on congested lines. The first concern is to determine the exact location of congested line for the placement of optimal size of DG so that cost can be minimised. In this work, hybridisation of firefly technique and differential evolution optimisation search has been proposed, which manages congestion effectively by rescheduling of generators satisfying the system constraints both technically and economically in the deregulated market scenario. To validate the proposed hybrid approach, results have been compared with firefly optimisation technique results. It is observed that the hybrid approach is an efficient tool in handling CM resulting in a secure operation to reduce flows in the heavily loaded lines with low system loss and increasing power capability with improved stability of network by controlling power flows in the network.     

Statistical analysis on results of optimal power sharing between linked microgrids

The future distribution network will be made of interconnected distribution systems, so-called microgrids (MGs). MGs provide an effective means of utilising energy from small-scale renewable resources. The probabilistic power generation behaviour renewable generations and load forecasting errors are the most important uncertainties in the MG operation. The proposed methodology of this paper applies these uncertainties into the operation problem in order to find the practical solutions. So, in this research the economic operation of multi-MGs is formulated as a cost-based objective function which is minimised using particle swarm optimisation algorithm. As a result, the problem outputs must be defined by probability distribution functions (PDFs) in order to achieve comprehensive analysis of the literature. Another contribution of the paper, which rises the accuracy of the analysis in operation discussion, is applying suitable fitting criteria to select the best PDF for each obtained result based on Akaike's information criterion.     

Stochastics multi-objective modelling of simultaneous reconfiguration of power distribution network and allocation of DGs and capacitors

This paper suggests a method based on stochastic multi-objective modelling for optimally siting and sizing of distributed generations and capacitors and simultaneously network reconfiguration, in order to take more advantages of recent developed technologies. With respect to the widespread effect of uncertainties of load forecasting and market price, some strategies must be devised so as to well incorporate them into the problem. The mentioned problem can be regarded as a multi-objective optimisation problem for which NSGA-II accompanied by fuzzy set theory are chosen. The main aim of DisCo is to provide highly reliable power at the lowest possible costs. Besides, voltage deviation and voltage stability as the criteria of power quality in distribution networks and emission pollutant reduction are treated as independent objective functions. Eventually, to validate the effectiveness of the proposed scheme, simulations are carried out and the results are compared to the initial configuration.     

Probabilistic prediction of urban water consumption using the SCEM-UA algorithm

Prediction of urban water consumption can help to improve the performance of water distribution systems. Despite the obvious presence of uncertainty in measurements and in assumed model types/structures, most of the existing water consumption prediction models are developed and used in a deterministic context. Methods for more realistic assessment of parameter and model prediction uncertainties have begun to appear in literature only recently. A novel application of the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) for the calibration of a water consumption prediction model is proposed here. The model is applied to a case study of the city of Catania (Italy) with the aim to predict daily water consumption. The SCEM-UA algorithm is used to calibrate the parameters of the artificial neural network based prediction model and in turn to determine the associated parameter and model prediction uncertainties. The results obtained using the SCEM-UA ANN approach were compared to the corresponding results obtained using other predictive models developed recently by the authors of the paper. When compared to the these models, the SCEM-UA ANN based water consumption prediction model shows similar predictive capability but also the ability to identify simultaneously the prediction uncertainty bounds associated with the posterior distribution of the parameter estimates.     

Generalized bridge network performance analysis with correlation and time-variant reliability

A novel approach and framework for the analysis of bridge networks is presented. The goal of the analysis is to assess the life-cycle performance of the network and its time-variant reliability. The proposed approach combines three important features that determine its ability to estimate with accuracy and robustness the reliability of a network along its life-cycle.The first one is that the reliability of the individual bridges is modeled as time-variant, due to the deterioration of their structural components. The network performance analysis is repeated for several time instants, with the relative reliability of the bridges. Therefore, the network performance indicators are also time-variant. In this way, the proposed procedure can be used as a basic tool for maintenance planning at the network level.The second asset is that the proposed framework takes into account complex (i.e. “generalized”) network layouts, not necessarily describable using series, parallel or series-parallel models. In fact, when all the possible traffic flows in a network and all the possible trip origins and destinations are considered, it is not feasible, in general, to model the network with a simple scheme. In the present paper, techniques derived from transportation engineering for the traffic flow distribution and assignment are used.The third feature is that the proposed approach considers a correlation structure among the states (in/out of service) of the various bridges of the network. In fact, bridges associated with the same network are likely to share similar characteristics and external loads. Therefore, a correlation structure for the service state of individual bridges is estimated and implemented in the analysis.A case study involving a transportation network with fourteen bridges is presented as a numerical application.     

Multi-objective modelling of simultaneous reconfiguration of NMG-based distribution network and operation of different DERs

ABSTRACT

A Micro-Grid (MG) is envisaged with ever increasing distributed energy resources (DERs) e.g. distributed generation, demand response and electrical vehicles. This paper suggests a method based on multi-objective modelling for day-ahead scheduling of Networked-MGs based distribution network in the presence of different DERs and also this method is able to find the daily reconfiguration instants. The proposed scheme is solved using NSGA-II from distribution network operator viewpoints, who is responsible for providing power demand in higher reliability level and lower costs. Besides, in the suggested scheme voltage deviation and voltage stability as efficient power quality criteria in distribution networks and emission pollutant reduction are incorporated as independent objective functions. Moreover, to quantify the influence of different load models, a 33-node distribution network is adopted with a load class mix of residential, industrial and commercial loads. Eventually, the obtained results are reported which verify the efficiency of the proposed method.     

城市给水管网系统模型的校核

对给水管网系统模型进行校核可提高其可信性和实用性,为此给出了模型校核的整体思路,在分析影响因素的基础上,提出了通过调整节点流量和海曾-威廉C值来减少变量个数的方案.算例表明该方法是可行的.     

Bayesian machine learning-based method for prediction of slope failure time

The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time (SFT). The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT. Currently, very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT prediction. In this paper, a comprehensive slope failure database was compiled. A Bayesian machine learning (BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction, through which the probabilistic distribution of the SFT can be obtained. This method was illustrated in detail with an example. Verification studies show that the BML-based method is superior to the traditional inverse velocity method (INVM) and the maximum likelihood method for predicting SFT. The proposed method in this study provides an effective tool for SFT prediction.     

Pavement defect detection with fully convolutional network and an uncertainty framework

Image segmentation has been implemented for pavement defect detection, from which types, locations, and geometric information can be obtained. In this study, an integration of a fully convolutional network with a Gaussian‐conditional random field (G‐CRF), an uncertainty framework, and probability‐based rejection is proposed for detecting pavement defects. First, a fully convolutional network is designed to generate preliminary segmentation results, and a G‐CRF is used to refine the segmentation. Second, epistemic and aleatory uncertainties in the model and database are considered to overcome the disadvantages of traditional deep‐learning methods. Last, probability‐based rejection is conducted to remove unreasonable segmentations. The proposed method is evaluated on a data set of images that were obtained from 16 highways. The proposed integration segments pavement distresses from digital images with desirable performance. It also provides a satisfactory means to improve the accuracy and generalization performance of pavement defect detection without introducing a delay into the segmentation process.     

Electricity distribution tariffs and distributed generation: Quantifying cross-subsidies from consumers to prosumers

An increasing amount of distributed generation (DG) can cause an increase or a decrease on distribution network costs. Tariff design is the main tool for allocating these costs to customers who own and operate DG resources.Currently, however, either DG units are exempt from paying distribution tariffs or they are subject to tariffs originally designed according to a traditional pricing model without DG in the grids, also known as load-based pricing. Partial recovery of the allowed distribution company revenue requirements or cross-subsidies between customers may ensue from such tariff arrangements.In this article, pricing, as represented by a combination of net metering and pure volumetric tariffs, is applied in the context of increasing DG. The paper presents a methodology where a Reference Network Model (RNM) is used to investigate the effect of this pricing scheme on the magnitude of cross-subsidies from consumers towards the so-called prosumers for a set of twelve simulations based on real-size networks in the U.S.For the considered scenarios, the analysis reveals substantial cross-subsidies from consumers toward prosumers. The degree of subsidy varies with the amount of DG connected to the grid and network characteristics. The rate of cross-subsidy tends to be higher for low-density grids.This paper contributes to the net metering literature with a quantitative assessment of cross-subsidies by comparing allocated payments to different actors with the costs they impose on the system, estimated through an RNM. Moreover, the paper proposed a tariff structure based on cost causality by proposing a cost-reflective, volumetric tariff approach through which aggregate load-driven and DG-driven network costs are accordingly allocated to loads and DG units.     

Optimal contracting strategies for public-lighting asset management:A case study from Iran

Public-lighting is a prominent subsector of the electricity distribution network. Removing upcoming challenges of the public-lighting system is an important necessity in network expansion planning. The public-lighting management structure is composed of three main participants: the regulatory unit, lighting managers, and private contractors. Each participant faces challenges in achieving its aims. This study investigates challenges in the areas of human resource management, cost estimation, price assignment, and time scheduling for private-sector contracting. Applying the reliability model of public-lighting lamps, the health status of lamps during the system operation is forecasted. In addition, to give a comprehensive solution for the mentioned challenges, the proposed strategies are optimized in terms of cost and risk. The results of this case study of the public-lighting system demonstrate the efficiency of the proposed method in alleviating the challenges of public-lighting managers, including optimal assignment of price and duration of contracts. In addition, the operating cost of the public-lighting system, an important challenge for lighting contractors, is minimized via a human resource management scheme.     

低矮房屋风致结构响应极值不确定性分析

风致结构响应极值估算在结构抗风的可靠度设计中十分重要。在整个极值估算过程中,由于许多不定或随机的因素存在(如：极值自身、估算方法、样本采集、极值概率模型等),得到的极值通常存在不确定性。在各种影响因素中,该文将考虑结构响应极值变量本身的随机特性,对任意分位点处响应极值的不确定性进行分析。首先,利用有限元软件对低矮房屋模型进行框架结构设计并优化,加载风压荷载得到结构响应时程数据。然后,基于Hermite多项式模型(HPM)转换过程方法,估算得到响应的极值Ⅰ型分布(Gumbel);基于该极值估算方法,提出时程样本偏度、峰度、零超越次数与Gumbel分布两个参数之间的经验公式。接着,考虑前四阶矩的不确定性,利用经验公式以及多步概率分析,对任意分位点处响应极值的不确定性进行估计。最后,给出相关结论。