Power quality disturbance mitigation in grid connected photovoltaic distributed generation with plug-in hybrid electric vehicle

In the last twenty years, electric vehicles have gained significant popularity in domestic transportation. The introduction of fast charging technology forecasts increased the use of plug-in hybrid electric vehicle and electric vehicles (PHEVs). Reduced total harmonic distortion (THD) is essential for a distributed power generation system during the electric vehicle (EV) power penetration. This paper develops a combined controller for synchronizing photovoltaic (PV) to the grid and bidirectional power transfer between EVs and the grid. With grid synchronization of PV power generation, this paper uses two control loops. One controls EV battery charging and the other mitigates power quality disturbances. On the grid connected converter, a multicarrier space vector pulse width modulation approach (12-switch, three-phase inverter) is used to mitigate power quality disturbances. A Simulink model for the PV-EV-grid setup has been developed, for evaluating voltage and current THD percentages under linear and non-linear and PHEV load conditions and finding that the THD values are well within the IEEE 519 standards

Impact of electric vehicles on the expansion planning of distribution systems considering renewable energy, storage and charging stations

Energy storage systems (ESS) have adopted a new role with the increasing penetration of electric vehicles (EV) and renewable energy sources (RES). EV introduce new charging demands that change the traditional demand profiles and RES are characterized by their high variability. This paper presents a new multistage distribution expansion planning model where investments in distribution network assets, RES, ESS, and EV charging stations are jointly considered. The charging demand necessary for EV transportation is performed using a vehicle model based on travel patterns. The variability associated with RES along with the demand requires the incorporation of uncertainty, which is characterized through a set of scenarios. These scenarios are generated by the k-means++ clustering technique that allows keeping the correlation in the information of the uncertainty sources. The resulting stochastic program is driven by the minimization of the present value of the total expected cost including investment, maintenance, production, losses, and non-supplied energy. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program, which can be solved by commercial software. Numerical results are presented for an illustrative 54-node test system

Challenges and pathways of low-carbon oriented energy transition and power system planning strategy: a review

This paper provides an overview of the challenges and pathways involved in achieving a low-carbon-oriented energy transition roadmap and power system planning strategy. The transition towards low-carbon energy sources is crucial in mitigating the global climate change crisis. However, this transition presents several technical, economic, and political challenges. The paper emphasizes the importance of an integrated approach to power system planning that considers the entire energy system (including both physical and information systems and market mechanisms) and not just individual technologies. To achieve this goal, the paper discusses various pathways toward low-carbon energy transition, including the integration of renewable energy sources into current energy systems, energy efficiency measures, and market-based and regulatory strategies encompassing the implementation of regulations, standards, and policies. Furthermore, the paper underscores the need for a comprehensive and coordinated approach to energy planning, taking into account the socio-economic and political dimensions of the transition process. In addition, the paper reviews the methodologies used in modeling low-carbon-oriented power system planning, including both model-based methods and advanced machine learning-assisted solutions. Overall, the paper concludes that achieving a low-carbon-oriented energy transition roadmap and power system planning strategy requires a multi-dimensional approach that considers technical, economic, political, and social factors

Análise do impacto da infraestrutura de recarga de veículos elétricos no planejamento da expansão dos sistemas de distribuição

With the increasing penetration of electric vehicles (EVs) the necessary charging infrastructure deployment becomes imperative. Thus, it is necessary to consider the increase in the demand associated with the EVs charging in the distribution systems expansion planning (DSEP), determining the necessary investments to meet the demand with competitive costs. In this way, methodologies to support the distribution system must be developed, considering the uncertainties related to both the conventional and the EV demand. Furthermore, other characteristics must be observed in relation to the insertion of EVs on the EDSs such as their location of connection and the charging type. In this context, the present work shows a two-stage stochastic model of mixed-integer linear programming (MILP) to solve the static expansion planning of distribution power system problem. The expansion planning assesses the construction and/or reinforcement of substations and circuits as well as the allocation of distributed generation units, capacitors banks, and electric vehicles public recharging stations (EVPS). The construction of scenarios considers a set of historic data to represent the stochastic features and the correlation between conventional and EV demand, wind and solar generation. To properly represent the historic EV demand data the Gaussian mixture models (GMM) methodology his used, modeling the main metrics of EVs recharge. To reduce the number of scenarios the present work makes use of the k-means clustering method generates. Finally, the importance of considering the uncertainties associated with the EVs demand in the DSEP is evaluated. As the recharge infrastructure development for EVs in public places is strategic some governments have embraced actions to install PSEVs. So, the present work also address the impact of the implementation profile of PSEVs on the DSEP. In this regard, two new constraints are proposed to compose the mathematical model of sizing the PSEVs inside the multistage expansion planning of distribution systems. This approach considers a deterministic MILP model that assesses the investment decisions over the considered planning horizon. To evaluate the results of the inclusion of those constraints a case of study is proposed. Both deterministic and stochastic models use the 18-bus test system in the simulations. The results show the importance of considering the characteristics concerning the insertion of EVs in the distribution systems, aiming to obtain expansion plans more adapted to the real conditions.Com a crescente penetração dos veículos elétricos (VEs), a necessidade de proporcionar uma infraestrutura de carregamento adequada será indispensável. Logo, é natural que o planejamento da expansão do sistema de distribuição (PESD) considere esse novo incremento da demanda associada à recarga dos VEs, pois é necessário determinar o investimento que garanta o atendimento da demanda com custos competitivos. Portanto, métodos que auxiliem o sistema de distribuição de energia elétrica (SDEE) a lidar com esse novo desafio devem ser desenvolvidos, considerando as incertezas associadas à demanda convencional, demanda dos VEs e geração renovável. Além disso, outras características devem ser observadas em relação a inserção dos VEs nos SDEEs, como o local da conexão e tipo de carregamento. Neste contexto, o presente trabalho apresenta um modelo de programação linear inteira mista (PLIM) estocástico de dois estágios, investimento e operação, baseado em cenários para resolução do problema de planejamento da expansão do sistema de distribuição estático. O planejamento da expansão avalia a construção e/ou reforço de subestações e circuitos, assim como também a alocação de unidades de geração distribuída, bancos de capacitores e estações públicas de carregamento dos VEs (EPVEs). Com o objetivo de representar as características estocásticas e a correlação entre demanda (convencional e dos VEs) e geração (eólica e fotovoltaica), considera-se um conjunto de dados históricos. Para representar adequadamente os dados de demanda dos VEs, utilizou-se um conjunto de modelos de mistura Gaussiana que modelam as principais métricas de recarga dos VEs. Para reduzir o número de cenários, o trabalho utiliza o método de clusterização k-means. Por fim, avalia-se a importância de considerar as incertezas associadas às demandas dos VEs no PESD. Como o desenvolvimento da infraestrutura de recarga de VEs em áreas públicas é estratégico e alguns governos vêm adotando medidas para instalação das EPVEs, o trabalho também aborda o impacto do perfil de implementação das EPVEs no PESD. Para isso, duas novas restrições são propostas para compor a modelagem matemática do dimensionamento das EPVEs dentro do problema de planejamento da expansão do sistema de distribuição multiestágio. Nesta abordagem, considera-se um modelo de PLIM determinístico que avalia as decisões de investimento ao longo horizonte de planejamento, e para avaliar o resultado da inclusão das novas restrições é proposto um estudo de caso. Ambos os modelos, determinístico e estocástico, utilizam o sistema teste de 18 barras nas simulações. Os resultados ressaltaram a importância de considerar as características em relação a inserção dos VEs nos SDEEs, visando obter planos de expansão mais adaptados às condições reais

Bidirectional Electric Vehicles Service Integration in Smart Power Grid with Renewable Energy Resources

As electric vehicles (EVs) become more popular, the utility companies are forced to increase power generations in the grid. However, these EVs are capable of providing power to the grid to deliver different grid ancillary services in a concept known as vehicle-to-grid (V2G) and grid-to-vehicle (G2V), in which the EV can serve as a load or source at the same time. These services can provide more benefits when they are integrated with Photovoltaic (PV) generation. The proper modeling, design and control for the power conversion systems that provide the optimum integration among the EVs, PV generations and grid are investigated in this thesis. The coupling between the PV generation and integration bus is accomplished through a unidirectional converter. Precise dynamic and small-signal models for the grid-connected PV power system are developed and utilized to predict the system’s performance during the different operating conditions. An advanced intelligent maximum power point tracker based on fuzzy logic control is developed and designed using a mix between the analytical model and genetic algorithm optimization. The EV is connected to the integration bus through a bidirectional inductive wireless power transfer system (BIWPTS), which allows the EV to be charged and discharged wirelessly during the long-term parking, transient stops and movement. Accurate analytical and physics-based models for the BIWPTS are developed and utilized to forecast its performance, and novel practical limitations for the active and reactive power-flow during G2V and V2G operations are stated. A comparative and assessment analysis for the different compensation topologies in the symmetrical BIWPTS was performed based on analytical, simulation and experimental data. Also, a magnetic design optimization for the double-D power pad based on finite-element analysis is achieved. The nonlinearities in the BIWPTS due to the magnetic material and the high-frequency components are investigated rely on a physics-based co-simulation platform. Also, a novel two-layer predictive power-flow controller that manages the bidirectional power-flow between the EV and grid is developed, implemented and tested. In addition, the feasibility of deploying the quasi-dynamic wireless power transfer technology on the road to charge the EV during the transient stops at the traffic signals is proven

Demand Curve Modeling for the Utility of the Future

Electricity systems are undergoing significant changes. Demands are shifting in magnitude and temporal distribution due to developing policies and technologies such as electric vehicles, heat pumps, embedded generation and energy storage, while an increasingly renewable supply is intermittent and less flexible. As such, there is currently great uncertainty in the industry and future business pathways may vary significantly from the current paradigm. This research focused on developing a set of models which can be used by utility companies to leverage their smart meter data and gain insights into possible future impacts and opportunities. The thesis presents a series of novel models, developed and implemented with data provided from a utility in Southern Ontario. First, a regression model was developed to leverage the full value of utility smart meter data by disaggregating residential and commercial sector demands into base, heating and cooling end uses. The use of a variable temperature changepoint only marginally improved prediction accuracy, but significantly shifted disaggregation results, particularly at hourly resolution. This model was also applied for weather normalization, assessment of technology change and projection under different climate scenarios. A second model used this and additional data from literature to project long term utility level average and peak seasonal load curves. A dynamic interface with parameterized controls allowed real-time visualization of technology and policy impacts on the demand curve. A set of eight literature-based scenarios were also projected to demonstrate the extreme range of impacts predicted by different literature. These led to the conclusion that unmanaged technology penetration can lead to significant challenges such as increased peaks, large ramp rates and lower utilization. An analysis was then performed at finer geographic resolution, investigating impacts on representative distribution system transformers. First, the current variation in local technology penetration was examined, showing a significantly skewed distribution with many transformers having up to ten times the average rates. Clustering was then used to identify a set of eight diverse, representative transformer load profiles. Future scenarios were modeled, demonstrating that the impacts of technology and optimal mitigation techniques vary significantly between regions of the distribution system. Finally, the dynamic utility load curve model was also updated to project demands for the representative transformer groups identified. This allows users to simultaneously assess local impacts and mitigation strategies, as well as aggregate effects on the overall system demands. Together these works combine to provide a valuable toolset and significant insight into potential system impacts

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method