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The adoption of Electric Vehicles (eV) as an alternate and sustainable replacement to the existing natural-resource powered vehicles paves the way to realize the vision for a cleaner and greener environment. Electric-Rickshaw (also known as e-Rickshaws) are a common mode of transportation in Indian traffic ecosystem and are mainly used for last mile connectivity. This article explores the feasibility of using Battery-Swap Technology as the charging mechanism for e-Rickshaws. The challenges associated with the technology, policy considerations and its economic impact have been presented in the paper. The promise shown by the suggested technology has been presented with reference to few case studies.

Distributed photovoltaic (PV) generator systems (especially rooftop PV) have been increasing significantly in electrical power systems. However, due to the variability of their output power, it is challenging to integrate large number of these PV generator systems into the existing electrical grid. These distributed PV generator systems can cause large voltage fluctuations due to the oscillation in their power output and reverse power flow in highly penetrated areas. While real power curtailment is an option to manage voltage issues, it reduces the production of this clean energy and is governed by policies and contracts. Traditionally, the use of reactive power to control voltage on a power grid is done through maintaining the voltage within a tolerable range by using transformer tap-changers (TPC), capacitor banks (CB), voltage regulators (VR), static synchronous compensator (STATCOM), and static Var compensators (SVC). Installation and maintenance costs of these devices can be quite high and some have relatively slow response times on the order of many seconds. A smart PV inverter can help regulate voltage by absorbing and injecting reactive power (Var) to/from the grid by using the Volt-Var control function. This paper presents an experimental analysis of the inverter Volt-Var control method for voltage regulation. The capacitive (i.e., Var injection) and inductive (i.e., Var absorption) effects of using a smart inverter and its ability to influence the voltage at the distribution level is investigated in this paper. When the smart PV inverter injects reactive power, it increases the distribution voltage. Conversely, voltage is reduced when the smart inverter absorbs reactive power. As a result, electric power utilities can control the distribution voltage without installing additional devices on the power network. The analyses from a field deployment under the Maui Advanced Solar Initiative Project in Hawaii is discussed here.

A battery energy storage system (BESS) is an available solution for utilities to deal with intermittency issues resulting from renewable energy resources. A BESS needs to have a control algorithm to provide a very good estimation of the load on the grid at each time step. A short-term load forecast (STLF) is necessary for efficient and optimized control of BESSs that are connected to the grid. In this work, two parallel-series techniques for load forecasting are proposed to optimize the performance of a grid-scale BESS (1 MW, 1.1 kWh) in 15-min steps within a moving 24-h window. In both techniques, a complex-valued neural network (CVNN) is used for parallel forecasting. The parallel component is based on the search for similar days of historical data that have a weekly index comparable to the forecast day. For series forecasting, historical data of each day is used within a moving forecast window by CVNN along with the spline method. For both techniques, parallel forecasting is mixed with series forecasting by an adjustment coefficient. Both techniques are tested on a set of real data for a grid with high PV penetration, and the obtained results are compared.

Demand response provides more flexibility in the operation of a power grid with high renewable energy penetration. In this paper, a mathematical model of storage devices and loads with thermal inertia, such as batteries, water heaters, and air conditioners, is presented. Dynamic and linear programming methods are used to solve the optimization problem subject to comfort constraints. The price signal for these devices can incorporate financial criteria as well as reliability criteria. Optimal operation of the storage devices and loads with thermal inertia are obtained considering the given price signal. Individual device performance optimization is compared with all of the devices as a single solution and optimal operation plots and results are presented and discussed. The presented optimization problem for house devices is solved using dynamic programming and mathematical programming and the results are compared.

In 2008, the State of Hawaii initiated a clean energy initiative that set an ultimate goal of 70% clean energy by 2030 (40% from renewable energy and 30% from energy efficiency). A controllable Battery Energy Storage Systems (BESSs) can be used to manage intermittent renewable resources on a power system to address both circuit and system level issues. Simulation and experimental results of applying a novel algorithm for the charging and discharging of a BESS are presented, using actual grid data for controlling a BESS for the purpose of peak load shaving, power curve smoothing, and voltage regulation of a distribution transformer. Two optimization objectives for peak shaving are presented in which proposed load forecasting methods are used. The application of a BESS for voltage regulation is examined and analyzed with different tests, and the observed results are discussed.

The determination of state-of-health (SOH) and state-of-charge (SOC) is challenging and remains as an active research area in academia and industry due to its importance for Li-ion battery applications. The estimation process poses more challenges after substantial battery aging. This paper presents an inline SOH and SOC estimation method for Li-ion battery packs, specifically for those based on LiFePO4 chemistry. This new hybridized SOC and SOH estimator can be used for battery packs. Inline estimated model parameters were used in a compounded SOC + SOH estimator consisting of the SOC calculation based on coulomb counting method as an expedient approach and an SOH observer using an extended Kalman filter (EKF) technique for calibrating the estimates from the coulomb counting method. The algorithm's low SOC and SOH estimation error, fast response time, and less-demanding computational requirement make it practical for on-board estimations. The simulation and experimental results, along with the test bed structure, are presented to validate the proposed methodology on a single cell and a 3S1P LiFePO4 battery pack.

As the world moves toward greenhouse gas reduction, there is increasingly active work around Li-ion chemistry-based batteries as an energy source for electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids. In these applications, the battery management system (BMS) requires an accurate online estimation of the state of charge (SOC) in a battery pack. This estimation is difficult, especially after substantial battery aging. In order to address this problem, this paper utilizes SOC estimation of Li-ion battery packs using a fuzzy-improved extended Kalman filter (fuzzy-IEKF) for Li-ion cells, regardless of their age. The proposed approach introduces a fuzzy method with a new class and associated membership function that determines an approximate initial value applied to SOC estimation. Subsequently, the EKF method is used by considering the single unit model for the battery pack to estimate the SOC for following periods of battery use. This approach uses an adaptive model algorithm to update the model for each single cell in the battery pack. To verify the accuracy of the estimation method, tests are done on a LiFePO 4 aged battery pack consisting of 120 cells connected in series with a nominal voltage of 432 V.

This paper examines the active power control of a photovoltaic (PV) power generation system. A traditional PV power generation system lacks controllability. However, smart PV inverters provide a mechanism to control the power output of PV generators. Distributed PV generator systems have been increasing for the past few decades as a clean renewable energy resource. As more distributed PV is interconnected to the power grid, the integration of these highly variable resources presents a challenging task for the utility. The power output of the PV generator system is proportional to the solar irradiance. Solar irradiance may vary due to water vapor, clouds, pollution, the season of the year and the time of day. As a result, the power output of a PV generator is a highly varying resource. Coupled with varying loads, the grid frequency and voltage become difficult to manage. One method to control voltage is by active power curtailment of the PV inverter. This paper presents analyses of active power curtailment and its effect on voltage in the Maui Advanced Solar Initiative Project where smart inverters were field deployed in a residential neighborhood.

The integration of distributed generations (DGs) in the electric power grid introduces distribution voltage (DV) fluctuations. Load demand variations are another source of DV fluctuations. The DV needs to be maintained within five percent of its nominal value. Several approaches exist for controlling the DV e.g. transformer tap-changers (TPCs), capacitor banks (CBs), voltage regulators (VRs), and static VAR compensators (SVCs). However, these DV control methods require installation and maintenance of new equipment for the electric power utilities. In addition, these devices have a relatively long response time. Due to the increase of DG integration, the DV needs to react promptly. Distributed photovoltaic (PV) generation have been increasing rapidly. The smart PV inverter enables several strategies to control the DV. For example, the smart PV inverter can absorb and generate reactive power (Var) using the Volt-Var control strategy to regulate the DV. This paper shows an experimental study of the Volt-Var control method using a smart PV inverter. The Volt-Var control strategy was tested using a smart SMA PV inverter. When the DV drops, the smart PV inverter dispatches Var to increase the DV. On the other hand, when the DV is higher than its nominal value, the smart PV inverter absorbs Var to decrease the DV. The real-time analysis of the Volt-Var control method was investigated in the Maui Advanced Solar Initiative project.

It is difficult to model the behavior of the battery pack accurately due to the electrochemical characteristics variations among cells of a battery pack. As a result, accurate state-of-charge (SOC) and state-of-health (SOH) estimation for the battery pack is a case provocation. The estimation process poses more challenges after substantial battery aging. This paper tries to estimate the SOC of a Li-ion battery pack for an electrical vehicle using improved extended Kalman filter (IEKF) which benefits from considering aging phenomenon in the electrical model of cells. In order to assemble a battery pack, we find cells with similar electrochemical characteristics. Model adaptive algorithm is applied on the corresponding cells of a string to minimize cell-to-cell variation's effect. During the operation, the values of electrical model of each cell are updated by the same algorithm to compensate aging effects on SOC estimation error. The mean value of updated cell's model is used for a single unit cell model of the pack used at IEKF to achieve more accurate SOC estimation. The algorithm's fast response and low computational burden, makes on-board estimation practical. The experimental results reveal that the proposed approach's SOC and voltage estimation errors do not exceed 1.5%.

A battery management system needs to have an accurate inline estimation of SOC for each individual cell in the battery pack. This estimation process poses challenges after substantial battery aging. This article presents a novel method based on model adaptive extended Kalman filter (MAEKF) to estimate SOC for Li-ion batteries. Sensitivity analysis of the electrical model verifies that the accuracy of SOC estimated by EKF is sensitive to resistors used in the cell's electrical model. In order to get the best estimation, values of resistors are obtained in an optimization process in the MAEKF. This method uses the fact of two sudden changes in the cell's voltage derivative with respect to time while discharging current is constant. These two points are assumed as reference points in which their SOC can be determined from cell's chemistry. The optimization algorithm uses the derivative of the cell's measured terminal voltage to allocate SOC of 92% and 15% for two reference points in the Vcell equation and updates cell's electrical model. The algorithm's process is fast and computationally inexpensive, making on-board estimation practical. The obtained results demonstrate that by using this method the estimated SOC error for aged Li-ion cells does not exceed 4%.

Power demand of isolated communities or islands primarily relies on heavy oil based diesel generator systems where these fossil fuel based power generators are not only costly but also create adverse effect on the environment. Photovoltaic generator system (PV system), wind energy conversion system (WECS) and battery energy storage system (BESS) are clean sources of alternative energy. These energy sources can be the alternative of diesel generator based power system for an isolated communities such as islands. However, both PV systems and WECSs are uncertain and fluctuating sources of energy causing power fluctuations and frequency deviations. This paper proposes a coordinated approach of the frequency control for a zero emission based small power system with renewable energy penetration. PV system, WECS and BESS have been utilized to control frequency of the proposed zero emission based power system. Efficacy of the proposed frequency control system is shown with simulations results.

This paper presents an evolutionary potential fuzzy clustering approach for on-line identification of an electronic component placement process in the pick-and-place machines. The main idea is to identify a global nonlinear fuzzy model of the component placement process. In the proposed approach, the fuzzy clustering method utilizes an on-line potential clustering scheme to form the antecedent part of fuzzy rule-based models using an evolving Takagi-Sugeno (eTS) structure adaptation algorithm. A new on-line approach is proposed to control the rate of rule creation mechanism. The effectiveness of the proposed approach is evaluated by comparing the achieved results with some well-known approaches.

In this paper, the simultaneous optimization of transmission and distribution grid has been proposed. The dynamic model of the grid consists of turbine governors (TG), automatic voltage regulators (AVR) as well as wind turbines, solar power units and energy storage units. Grid stability is obtained by line stability and voltage stability indices and also by conducting eigenvalue analysis of the complete power system grid. Particle Swarm Optimization (PSO) algorithm adjusts the grid control settings (active and reactive power management) to maximize the renewable penetration at all layers of the grid. IEEE 14 bus system in conjunction with a micro grid is used as the test system. The results demonstrate that simultaneous reactive power management of transmission and distribution grid can maximize the renewable penetration at transmission and distribution layers of the grid. However different case studies with different renewable power pro-files at each layer of the grid are remained to be optimized, this study recommends that real-time management of reactive power at all layers of the grid can be a good candidate to deal with intermittency of renewable sources.