Chenghong Gu

Long-Run Marginal Cost Pricing Based on Analytical Method for Revenue Reconciliation

Incremental and marginal approaches are two different types of methods to price the use of networks. The major difference between them is in the way they evaluate the costs imposed by network users. The former calculates network charges through simulation and the latter derives charges with a sensitivity-based analytical approach. Both charging models aim to send cost-reflective economic signals to customers, providing an economic climate for the cost-effective development of networks. In this paper, a novel long-run marginal cost (LRMC) pricing methodology based on analytical method is proposed to reflect the impacts on the long-run costs imposed by a nodal injection through sensitivity analysis. The sensitivity analysis consists of three partial differentiations: (1) the sensitivity of circuit power flow with respect to nodal power increment, (2) the sensitivity of the time to reinforce network with respect to changes in circuit power flows, and (3) the sensitivity of present value of future reinforcement with respect to changes in time to reinforce. Two test systems are employed to illustrate the principles and implementation of the proposed method. Results from incremental and marginal approaches under different system conditions are compared and contrasted in terms of charges and tariffs. The proposed method, as demonstrated in the test systems, can produce forward-looking charges that reflect the extent of network utilization levels in addition to the distance that power must travel from points of generation to points of consumption. Furthermore, the proposed method is able to provide further insights into factors influencing network charges.

Cost/Benefit Assessment of a Smart Distribution System With Intelligent Electric Vehicle Charging

In the near future, with more distributed generators connected and new demands arising from the electrification of heat and transport in the distribution networks, infrastructure will become ever more stressed. However, building costly new circuits to accommodate generation and demand growth is time-consuming and environmentally unfriendly. Therefore, active network management (ANM) has been promoted in many countries, aiming to relieve network pressure. Previous research in ANM was focused on distribution areas with significant renewable penetration, where ANM reduced network pressure through significantly enhanced generation curtailment strategies rather than adopting traditional asset investment. This paper proposes the use of electric vehicles (EVs) as responsive demand to complement network stress relief that was purely based on generation curtailment. It is achieved by allowing EVs to absorb excessive renewable generation when they cause network pressure, and it thus can provide additional measures to generation curtailment strategies. The approach is illustrated on a practical extra-high voltage distribution system. The analyses clearly demonstrate the combined management of demand and generation is superior to previous sole generation management. The combined management strategy can achieve 7.9% improvement in utilization of renewable energy, and subsequently increase the net investment profit by £566 k.

Development of Low Voltage Network Templates—Part I: Substation Clustering and Classification

In order to improve low voltage (LV) network visibility without extensive monitoring and integrate low carbon technologies (LCTs) in a cost-effective manner, this paper proposes a novel three-stage network load profiling method. It uses real-time information monitored from selective representative areas to develop network templates. The three stages are: clustering, classification and scaling. It can be used to identify the loading conditions of unmonitored LV systems with similar fixed data to those monitored LV substations. In the clustering stage, hierarchical clustering and K-means are used to cluster substations into groups based on the shape of the monitored load profiles. The classification tool designed with multinomial logistic regression maps an unmonitored LV substation into the most probable templates by using routinely available fixed data. Finally, clusterwise weighted constrained regression is employed to estimate peak for individual LV substations and the developed templates. The three-stage profiling is demonstrated on a practical system in the U.K. under the umbrella of a smart grid trail project. Ten LV templates are developed by using the metered data from 800 monitored LV substations. A comprehensive comparison between the estimated peaks using the three-stage process and the metered peaks suggests that the methodology can achieve superior accuracy. This is part I of the paper, introducing clustering and classification. The scaling (peak estimation) process will be introduced in part II of the paper.

New Problem Formulation of Emission Constrained Generation Mix

This paper proposes an enhanced optimization formulation to help determine the type of power generation mix that can meet a given carbon emission target at the minimum cost. Compared to the previous studies, the model proposed in this paper takes account of the emission cost at operational level and explores its impacts on the long-term emission target oriented generation planning innovatively. Meanwhile, the model is able to take account of the integer variables and nonlinearity of the operational cost together with network constraints and renewable generation expansion in one long-term generation planning model. The problem is solved by an innovative discrete gradient search method, and a new concept, emission reduction cost (ERC), is developed, which helps determine which generation technology is the most cost efficient in emission reduction during different stages of generation expansion. A case study on a modified IEEE 30-bus system is presented to demonstrate the application of this model and the value of considering short-term emission costs and the network constraints on the long-term generation expansion. The results and sensitivity analysis are provided to show that a higher short-term financial pressure can help realize the emission target at a lower total cost (investment and operational costs). Optimization without considering it may overestimate the total cost required for the generation mix restructuring. Additionally, a comparative study shows that optimization without considering network constraints may underestimate the total cost required for realizing the specified emission reduction target.

Evaluation of investment deferral resulting from microgeneration for EHV distribution networks

Microgeneration (MG) comes in various forms, ranging from solar photovoltaic (PV), wind turbines, small hydro to solar water heating and among others. The governments in Europe see MG as a real alternative in reducing carbon emission and improving supply efficiency and security. Incentives for MGs are therefore on the rise, along with the number of units connected to the HV/LV distribution networks. These incentives typically bear no relation to the impact that MGs would have on the infrastructure network and on the generation supply. These un-directed incentives could bring unnecessary burdens to the energy system rather than help. Therefore, it is desirable to develop cost-effective incentives for MGs that can reflect the potential benefits/costs brought by MGs. Investment deferral is considered to be one of the most important benefits brought by integrating MGs into the distribution network. In this paper, the investment deferral is evaluated and quantified by connecting MGs at various locations and at differing penetration and concentration levels. This paper aims to achieve three goals: 1) to propose a method to assess investment deferral resulting from MGs for the EHV (Extra High Voltage) network; 2) to investigate how investment deferral varies with different MG allocation approaches in the network; 3) to suggest a more effective allocation approach which can bring more benefits to network investment deferral when the same quantity of MGs is connected. All the analyses are carried out on a subset of a practical system in the UK.

Implementation of load profile test for electricity distribution networks

Load profiles play an important role in electricity industry. They are widely used in tariff design and system operation planning. In the UK, the load profiles currently used by the industry were developed in 1990s. Although they have served the industry well for decades, the increasing number of low carbon customers may cause the actual load profiles to deviate from the original benchmarks. Thus, these existing load profiles may not be able to appropriately reflect the energy usage patterns nowadays. Therefore, it is necessary to test the accuracy and applicability of the existing classic load profiles and further refine them to better mimic the actual power consumption patterns. Theoretically, it is more accurate to obtain load profiles by measuring the power consumption of all customers in each class; however, the reality is that it is often too expensive or impractical to collect and analyze the load data for an entire area. This report proposes a power synthesis approach to testify and analyze the representativeness of the existing load profiles in the new power environment. The load profiles currently used by the UKs industry are first introduced and they are further tested with the recorded data taken from Dowlishford substation in Southwest England. Obtained results show that the current load profiles are out of date, and the corresponding errors are identified and the causes are analyzed.

A Novel Dispatching Control Strategy for EVs Intelligent Integrated Stations

In order to provide a cost-effective solution for accommodating the increasing electric vehicles (EVs) and maximizing their benefits to the grid, a novel EV intelligent integrated station (IIS) making full use of ex-service batteries, is proposed in this paper. It first presents the framework and characteristics of IISs by describing its components, including a dispatching center, multipurpose converter devices, a charge exchange system, and an echelon battery system. The grid status, batteries exchanging requests, and energy capacity of IISs are monitored timely to offer inputs for its optimal operation. The concept of generalized energy is thereby introduced to systematically understand the energy/power flow between IISs and EVs, as well as between IISs and power grids. Then, a novel charging and discharging control strategy for managing EVs is presented. Compared with existing approaches, the proposed control strategy can offer peak load shifting when meeting EV battery charging/exchanging requests. The experimental results demonstrate the effectiveness and benefits of the control strategy in terms of providing peak load shifting for the power grid. This integrated station concept can maximize the benefits of EVs, and the retired batteries more flexibly and effectively.

Application of long-run network charging to large-scale systems

Charging methodology is one important scheme in the deregulated environment in the way that it can be utilized to recover the investment cost from network users according to their different impact on the network. The long-run incremental cost (LRIC) pricing methodology developed by University of Bath in conjunction with Western Power Distribution (WPD, UK) and Ofgem (the office of gas and electricity markets, UK) has drawn lots of attention from industry and academic circles and found its application in practice. Compared with the existing long-run cost pricing methodologies, this charging model can produce forward-looking charges that reflect both the extent of the network needed to serve the generation/demand and the degree to which the network is utilized. This paper examines the practical issues concerning implementation of this charging model in order to assist its utilization in the future. Firstly, the calculation and selection of the parameters, load growth rate, contingency factor, asset costs, that would impact charge evaluation are discussed, followed by the focus on some particular issues concerning them. Thereafter, the technical problems which might appear while applying this charging model to large-scale practical systems are dressed and a few feasible solutions are provided. This charging model, at last, is demonstrated on a practical system taken from the U.K. network.

Novel Cost Model for Balancing Wind Power Forecasting Uncertainty

The intermittency of wind generation creates nonlinear uncertainties in wind power forecasting (WPF). Thus, additional operating costs can be incurred for balancing these forecasting deviations. Normally, large wind power penetration requires accurate quantification of the uncertainty-induced costs. This paper defines this type of costs as wind power uncertainty incremental cost (WPUIC) and wind power uncertainty dispatch cost (WPUDC), and it then formulates a general methodology for deriving them based on probabilistic forecasting of wind power. WPUIC quantifies the incremental cost induced from balancing the uncertainties of wind power generation. WPUDC is a balancing cost function with a quadratic form considering diverse external conditions. Besides, the risk probability (RP) of not meeting the scheduled obligation is also modelled. Above models are established based on a newly developed probabilistic forecasting model, varying variance relevance vector machine (VVRVM). Demonstration results show that the VVRVM and RP provide accurate representation of WPF uncertainties and corresponding risk, and thus they can better support and validate the modelling of WPUDC and WPUIC. The proposed cost models have the potential to easily extend traditional dispatches to a new low-carbon system with a high penetration of renewables.

Adaptabilities of three mainstream short-term wind power forecasting methods

Variability and intermittency of wind is the main challenge for making a reliable wind power forecasting (WPF). Meteorological and topological complexities make it even harder to fit any forecasting algorithm to one particular case. This paper presents the comparison of three short term WPF models based on three wind farms in China with different terrains and climates. The sensitivity effects of training samples on forecasting performance are investigated in terms of sample size, sample quality, and sample time scale. Then, their adaptabilities and modeling efficiency are also discussed under different seasonal and topographic conditions. Results show that (1) radial basis function (RBF) and support vector machine (SVM) generally have higher prediction accuracy than that of genetic algorithm back propagation (GA-BP), but different models show advantages in different seasons and terrains. (2) WPF taking a month as the training time interval can increase the accuracy of short-term WPF. (3) The change of sample number for the GA-BP and RBF is less sensitive than that of the SVM. (4) GA-BP forecasting accuracy is equally sensitive to all size of training samples. RBF and SVM have different sensibility to different size of training samples. This study can quantitatively provide reference for choosing the appropriate WPF model and further optimization for specific engineering cases, based on better understanding of algorithm theory and its adaptability. In this way, WPF users can select the suitable algorithm for different terrains and climates to achieve reliable prediction for market clearing, efficient pricing, dispatching, etc.