Distribution transformer loading in unbalanced three-phase residential networks with random charging of plug-in electric vehicles

Utilization of plug-in electric vehicles (PEVs) is gaining popularity in recent years due to the growing concerns about fuel depletion and the increasing petrol price. Random uncoordinated charging of multiple PEVs in residential distribution feeders at moderate penetration levels are expected in the near future. The potential for stresses and network congestion is significant as PEV charging activities represent sizeable loads with unpredictable locations. Furthermore, the forthcoming smart grids will be unbalanced due to non-uniform distributions of PEVs in the three phases with unpredictable charging rates, times and durations. This paper explores the detrimental impacts of random PEV charging on the distribution transformer loading and bus voltage profiles of unbalanced smart grids. The impacts of non-uniform distributions of PEVs on the three phases, as well as deferred plugging of vehicles (encouraged by introducing higher electricity prices during the peak hours) are also explored. Simulation results will be generated and analyzed for an unbalanced three-phase 62 node residential network populated with PEV chargers using Matlab/Simulink software

Reallocating charging loads of electric vehicles in distribution networks

In this paper, the charging loads of electric vehicles were controlled to avoid their impact on distribution networks. A centralized control algorithm was developed using unbalanced optimal power flow calculations with a time resolution of one minute. The charging loads were optimally reallocated using a central controller based on non-linear programming. Electric vehicles were recharged using the proposed control algorithm considering the network constraints of voltage magnitudes, voltage unbalances, and limitations of the network components (transformers and cables). Simulation results showed that network components at the medium voltage level can tolerate high uptakes of uncontrolled recharged electric vehicles. However, at the low voltage level, network components exceeded their limits with these high uptakes of uncontrolled charging loads. Using the proposed centralized control algorithm, these high uptakes of electric vehicles were accommodated in the network under study without the need of upgrading the network components

Modelling of residential side flexibility for distribution network planning

With the environmental impacts of the fossil fuel economy being more and more visible it became oblivious that action against further climate change needs to be taken. This led to theenergy transition effort undertaken by countries of the European Union with the goal of increased usage of sustainable energy at the costofnon-renewable fuel sources. And on the national level,it led to more regionalized targets.With this in mind, the Netherlands adopted several goals with a target of reducing dependence on fossil fuels. These ranged from a bigger percentage of renewable energy in energy supply, through electrification of heating, to widespread adoption of electric vehicles. All of theseintroduce changes to how the energy system is operated. And this is particularly visible forelectricitydistribution system operators. These new developmentscouldmeanthatthegrid assets that were previously assumed to be functioning for the next decades would be retired earlier than expected. However, progressin areas of flexibility in electrical energy consumption present opportunityfor deferred replacement of those otherwise prematurely retired assets.In this context, the main objective of this thesis was to assess the benefit thatactivation of electrical energy flexibility in households could bring to thedistribution system operator. Between two energy transition scenarios consideredand different simulation settings,it was discovered that from 3.3 to 35.4% cumulative investments into grid assets could be deferred in next 8 to 10 years into the future,for considered networks. This corresponds tobetween1.1 and16.7 million € for examined networks,whichcontained about 5% of assets(transformers,medium and low voltage cables)belonging tothe Dutchdistribution system operator Enexis. However, in order to arrive at these values,the followingsteps had to be taken.Firstly, possible methods used to activate flexibility were researchedand compared. These included tariff-and market-based solutions, connection agreements and direct control approach. Based on the review of current literature and pilot projects it was decided that power-based tariffs werethe most aligned with the goal of reducing the impact onto the DSO’s grid assetswithpresented requirements.This decision was taken dueto the cost-reflectiveness of network asset usagepresented by power-based tariffs. It was further reinforced by the factthe main criterion considered during asset sizingis expected loadingsince in medium and low voltage networks peak power corresponds to the majority of costs. Beside technical effectiveness,the power-based tariffwas found to promiseopportunity in other aspects. Thosewere social acceptance, influenced by customers alreadybeingaccustomed to the tariff system,the readinessof technology behind this approachand compliance with the legislative framework.Secondly, based on the outcome of the previous step it was decided to model the impact of the power-based tariff onto the grid assets. In order to analyse the impact of the potential solutiononto the real grid assets, the modelwas incorporatedinto the Enexis’ Scenariotool -bottom-up scenario analysis tool developed for short to medium-term network planning purposes. This decision posed a strict requirement onto a high computational performance in order to allow examination at network scale withinthe feasible timescale. The proposed model focused onsimulating the possible impact of the power-based tariff on the residential load profiles with a focus on electric vehicle charging and photovoltaic panel generation. Thirdly, model results were examined from the single household level up to multiple low voltage networks and connecting medium voltage network fragments. Examinationsat the network level were run for multiple sets of possible scenarios. Then based on the comparison with the baseline scenario, ones without activation of flexibility, assets for which deferred replacement is possible were identified. These deferral possibilities were later translated into the monetary values of cumulativesavingsup to a given year of simulation, resulting in the figures presented in the beginning.In conclusion,this project identified optimalmethod, from the viewpointof DSO,for activation of flexibility from the households, presented model that modifies residential loads according to this method and performed an economic evaluation of the tariff’s impact onto the part of DSO’s gridOutgoin

Optimal planning of distribution grids considering active power curtailment and reactive power control

In this paper, a new planning methodology is proposed for existing distribution grids, considering both passive and active network measures. The method is designed to be tractable for large grids of any type, e.g., meshed or radial. It can be used as a decision-making tool by distribution system operators which need to decide whether to invest in new hardware, such as new lines and transformers, or to initiate control measures influencing the operational costs. In this paper, active power curtailment and reactive power control are taken into account as measures to prevent unacceptable voltage rises as well as element overloads, as these allow postponing network investments. A low-voltage, meshed grid with 27 nodes is used to demonstrate the proposed scheme. In this particular case, the results show that by using control measures, an active distribution system operator can defer investments and operate the existing infrastructure more efficiently. The methodology is able to account for variations in operational and investment costs coming from regulatory influences to provide an insight to the most cost-efficient decision

Practical application of digital computer to distribution systems

Given a three phase ac power distribution system, a digital computer program is presented which determines a complete steady state solution for a given condition. The network data is processed by the computer into an admittance matrix, and these parameters are stored in a manner that enables the computer program to operate upon them as the coefficient of a simultaneous set of nonlinear equations. This set of equations are solved by the Gauss-Seidel Iterative Process improved by a modification of the relaxation method. Practical experience with the distribution system of the Davenport, Iowa, plant of the Aluminum Company of America (Alcoa), is also described. Results are briefly discussed with relevance to the importance of the inclusion of an improved data for future work --Abstract, page ii

Demand Response Benefits for Major Assets of High Voltage Distribution Systems - Capacity Gain and Life Management

Power systems require an adequate capacity and higher utilization efficiency for an economic and reliable supply of electricity. However, their utilization efficiency is ordinary owing to low load factor and reserve capacity needs. Moreover, the growth of electricity demand and aging infrastructure call for massive investments in form of expansions and replacements. Therefore, the power industry is searching for novel solutions to deal with the future needs. Demand response (DR), a load shaping tool in smart grids, can be a potential solution to the future needs. The aim of the dissertation is to assess the DR benefits of capacity utilization gain and better life management for major assets of high voltage grid. The study focuses on subtransmission grids because they have captured least attention in the prior research. Primary substation transformers have given special attention here due to their vital position in the system and high component cost. The aim of the dissertation is further divided into three tasks in order to distinguish the DR benefit among phases of operations and planning and various components. The first task proposes optimization models for utilization gain and life management of transformers by DR during normal and contingency operations. The second task offers tools for optimal capacity planning of transformers in primary distribution substations with and without considering DR. These tools incorporate all transformer related costs, their failure rate increase with age, and their salvage value based on loss-of-life. The third task determines the potential of DR in mitigating the redundancy needs of lines/cables, transformers, and busbars by comparing outage cost due to their contingencies. The simulations are performed using the developed models for typical Finnish systems. The results indicate the following notable deductions. The utilization efficiency of grid components can be substantially improved using DR that depends upon load shape and its DR capability. Also, DR offers significant better life management potential for transformers during both nor- mal and contingency operations. Moreover, the employment of DR along with remote switch- ing of load transfer between substations provides superior savings in transformer capacity planning as compared to that of manual load shifting. Furthermore, the optimal decisions of DR activations are essential in order to gain the intended DR benefits at a minimal expense. The power system utilities can use the models of this dissertation for making decisions of DR deployments. These deployments will be helpful in delaying or eliminating the capacity investments. Moreover, the tools of the second task will help asset managers for taking optimal planning decisions of transformer ratings and their replacement and maintenance schedules.Voimajärjestelmät tarvitsevat riittävästi kapasiteettia ja korkean käyttöasteen taatakseen taloudellisen ja luotettavan sähkön saannin. Järjestelmän potentiaalia ei saada kuitenkaan hyödynnettyä täydellisesti matalan käyttökertoimen ja reservivaatimusten takia, minkä lisäksi kasvava sähkön kysyntä ja ikääntyvä järjestelmä lisäävät painetta investointeihin. Tämän takia sähköteollisuus on kiinnostunut uusista ratkaisuista, joilla järjestelmäresurssit saadaan tehokkaampaan käyttöön. Älykkäiden sähköverkkojen tarjoama kysyntäjousto (demand response, DR) nähdään yhtenä tällaisena ratkaisuna. Tämän väitöskirjan tarkoituksena on tutkia kysyntäjouston hyötyä siirtoverkon kapasiteetin hyödyntämisessä sekä eliniän hallinnassa. Työ keskittyy suurjännitejakeluverkkoon, jonka osalta aihetta ei ole vielä juuri tutkittu. Erityisesti keskitytään muuntajiin niiden tärkeyden ja korkean kustannuksen takia. Jotta kysyntäjouston tarjoama hyöty voidaan erotella tarkemmin verkon eri toimintojen, suunnittelun ja komponenttien kesken, väitöskirja on jaettu kolmeen osaan. Ensimmäinen osa esittelee optimointimallin muuntajan käyttöasteen parantamiseen ja eliniän pidentämiseen kysyntäjouston avulla normaalikäytön aikana sekä vikatilanteissa. Toinen osa tarjoaa työkaluja muuntajan optimaalisen kapasiteetin mitoittamiseen kysyntäjouston kanssa ja ilman. Kolmannessa osassa tutkitaan kysyntäjouston potentiaalia vähentää järjestelmän ylimitoittamista vertailemalla keskeytyskustannuksia. Työssä suoritettavat simuloinnit tehdään Suomen järjestelmää kuvaavalla mallilla. Tulokset osoittavat, että verkostokomponenttien hyödyntämistä voidaan tehostaa huomattavasti kysyntäjoustolla riippuen kuormituksen vaihtelusta ja sen tarjoamasta joustosta. Kysyntäjousto vähentää myös huomattavasti muuntajien vanhenemista normaalin käytön ja vikatilanteiden aikana. Kysyntäjousto optimaalinen aktivointi on kuitenkin olennaista, jotta halutut hyödyt voidaan saavuttaa. Järjestelmävastaavat voivat käyttää työssä esiteltyjä malleja, jos he suunnittelevat kysyntäjouston hyödyntämistä esimerkiksi investointipäätösten yhteydessä. Lisäksi työn toisessa osassa esiteltävät työkalut auttavat suunnittelijoita muuntajien optimaalisessa mitoittamisessa ja ylläpidossa

TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system

EcoBlock: Grid Impacts, Scaling, and Resilience

Widespread deployment of EcoBlocks has the potential to transform today's electricity system into one that is more resilient, flexible, efficient and sustainable. In this vision, the system will consist of self- su cient, renewable-powered, block-scale entities that can deliberately adjust their net power exchange and can optimize performance, maintain stability, support each other, or disconnect entirely from the grid as needed. This report is intended as an independent analysis of the potential relationships, both constructive and adverse, between EcoBlocks and the grid

A new Risk-Managed planning of electric distribution network incorporating customer engagement and temporary solutions

The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary non-network solutions (NNSs) such as demand response (DR) and temporary energy storage system/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the “optimal level of demand” for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward-backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach