Optimal Voltage Regulation of Unbalanced Distribution Networks with Coordination of OLTC and PV Generation

Photovoltaic (PV) smart inverters can regulate voltage in distribution systems by modulating reactive power of PV systems. In this paper, an optimization framework for optimal coordination of reactive power injection of smart inverters and tap operations of voltage regulators for multi-phase unbalanced distribution systems is proposed. Optimization objectives are minimization of voltage deviations and tap operations. A novel linearization method convexifies the problem and speeds up the solution. The proposed method is validated against conventional rule-based autonomous voltage regulation (AVR) on the highly-unbalanced IEEE 37 bus test system. Simulation results show that the proposed method estimates feeder voltage accurately, voltage deviation reductions are significant, over-voltage problems are mitigated, and voltage imbalance is reduced.Comment: IEEE Power and Energy Society General Meeting 201

Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management via Model Predictive Mixed Integer Control

In this paper, a model predictive mixed integer control method for BYD Qin Plus DM-i (Dual Model intelligent) plug-in hybrid electric vehicle (PHEV) is proposed for co-optimization to reduce fuel consumption during car following. First, the adaptive cruise control (ACC) model for energy-saving driving is established. Then, a control-oriented energy management strategy (EMS) model considering the clutch engagement and disengagement is constructed. Finally, the co-optimization structure by integrating ACC model and EMS model is created and is converted to the mixed integer nonlinear programming (MINLP). The results show that this modeling method can be applied to EMS based on the model predictive control (MPC) framework and verify that co-optimization can achieve a 5.1$\%$ reduction in fuel consumption compared to sequential optimization with the guarantee of ACC performance

μ-Benzene-1,2,4,5-tetra­carboxyl­ato-κ4 O 1,O 2:O 4,O 5-bis­[diaqua(phen­an­thro­line-κ2 N,N′)nickel(II)] 0.67-hydrate

The asymmetric unit of the title compound, [Ni2(C10H2O8)(C12H8N2)2(H2O)4]·0.67H2O, contains one complete binuclear complex and one half-mol­ecule, the latter being completed by crystallographic inversion symmetry, and 0.67 of a solvent water molecule. Each Ni2+ cation is coordinated by a 1,10-phenanthroline ligand, a bidentate benzene-1,2,4,5-tetra­carboxyl­ate (btec) tetra-anion and two water mol­ecules to generate a distorted cis-NiN2O4 octa­hedral coordination geometry. The btec species bridges the metal ions. In the crystal, the clusters and uncoordinated water mol­ecules are linked by O—H⋯O hydrogen bonds and π–π inter­actions [shortest centroid–centroid separation = 3.596 (2) Å] to form a three-dimensional network

Combining offline and online machine learning to estimate state of health of lithium-ion batteries

This article reports a new state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with cell inconsistency and online implementability are addressed using a proposed individualized estimation scheme that blends a model migration method with ensemble learning. A set of candidate models, based on slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are first trained offline by choosing a single-point feature on the incremental capacity curve as the model input. For online operation, the prediction errors due to cell inconsistency in the target new cell are next mitigated by a proposed modified random forest regression (mRFR) for high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably high SOH estimation accuracy with only a small amount of early data and online measurements are needed for practical operation

Fast charging control of Lithium-ion batteries: Effects of input, model, and parameter uncertainties

The foundation of advanced battery management is computationally efficient control-oriented models that can capture the key battery characteristics. The selection of an appropriate battery model is usually focused on model order, whereas the effects of input and parameter uncertainties are often overlooked. This work aims to pinpoint the minimum model complexity for health-conscious fast charging control of lithiumion batteries in relation to sensor biases and parameter errors. Starting from a high-fidelity physics-based model that describes both the normal intercalation reaction and the dominant side reactions, Pad\ue9 approximation and the finite volume method are employed for model simplification, with the number of control volumes as a tuning parameter. For given requirements on modeling accuracy, extensive model-based simulations are conducted to find the simplest models, based on which the effects of current sensor biases and parameter errors are systematically studied. The results show that relatively loworder models can be well qualified for the control of voltage, state of charge, and temperature. On the other hand, high-order models are necessary for health management, particularly during fast charging, and the choice of the safety margin should also take the current sensor biases into consideration. Furthermore, when the parameters have a certain extent of uncertainties, increasing the model order will not provide improvement in model accuracy

Nonlinear Model Inversion-Based Output Tracking Control for Battery Fast Charging

We propose a novel nonlinear control approach for fast charging of lithium-ion batteries, where health- and safety-related variables, or their time derivatives, are expressed in an input-polynomial form. By converting a constrained optimal control problem into an output tracking problem with multiple tracking references, the required control input, i.e., the charging current, is obtained by computing a series of candidate currents associated with different tracking references. Consequently, an optimization-free nonlinear model inversion-based control algorithm is derived for charging the batteries. We demonstrate the efficacy of our method using a spatially discretized high-fidelity pseudo-two-dimensional (P2D) model with thermal dynamics. Conventional methods require computationally demanding optimization to solve the corresponding fast charging problem for such a high-order system, leading to practical difficulties in achieving low-cost implementation. Results from comparative studies show that the proposed controller can achieve performance very close to nonlinear and linearized model predictive control but with much lower computational costs and minimal parameter tuning efforts

Model-based state of charge estimation algorithms under various current patterns

Numerous model-based techniques have been proposed to estimate the state of charge (SOC) of lithium-ion batteries. In automotive applications, the algorithms are subjected to changing load profiles, requiring investigations into their general performance under various working conditions. In this study, three different load patterns derived from a customized dynamic driving profile, a standard driving cycle, and a constant discharge are used for the experimental verification. Four selected algorithms including the Ampere-hour counting, the extended Kalman filter, the particle filter, and the recursive least square filter are implemented. Their performance in terms of accuracy and robustness are compared. In addition, the load profile is analyzed in the frequency domain. The results show that the filter performance is dependent on the current patterns and can be correlated to the frequency spectrum of the load profile

Dynamic Weight-Based Collaborative Optimization for Power Grid Voltage Regulation

Power distribution grids with high PV generation are exposed to voltage disturbances due to the unpredictable nature of renewable resources. Smart PV inverters, if controlled in coordination with each other and continuously adapted to the real-time conditions of the generation and load, can effectively regulate nodal voltages across the feeder. This is a fairly new concept and requires communication and a distributed control logic to realize a fair utilization of reactive power across all PV systems. In this paper, a collaborative reactive power optimization is proposed to minimize voltage deviation under changing feeder conditions. The weight matrix of the collaborative optimization is updated based on the reactive power availability of each PV system, which changes over time depending on the cloud conditions and feeder loading. The proposed updates allow PV systems with higher reactive power availability to help other PV systems regulate their nodal voltage. Proof-of-concept simulations on a modified IEEE 123-node test feeder are performed to show the effectiveness of the proposed method in comparison with four common reactive power control methods

Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks

Photovoltaic (PV) smart inverters can improve the voltage profile of distribution networks. A multi-objective optimization framework for coordination of reactive power injection of smart inverters and tap operations of on-load tap changers (OLTCs) for multi-phase unbalanced distribution systems is proposed. The optimization objective is to minimize voltage deviations and the number of tap operations simultaneously. A novel linearization method is proposed to linearize power flow equations and to convexify the problem, which guarantees convergence of the optimization and less computation costs. The optimization is modeled and solved using mixed-integer linear programming (MILP). The proposed method is validated against conventional rule-based autonomous voltage regulation (AVR) on the highly-unbalanced modified IEEE 37 bus test system and a large California utility feeder. Simulation results show that the proposed method accurately estimates feeder voltage, significantly reduces voltage deviations, mitigates over-voltage problems, and reduces voltage unbalance while eliminating unnecessary tap operations. The robustness of the method is validated against various levels of forecast error. The computational efficiency and scalability of the proposed approach are also demonstrated through the simulations on the large utility feeder.Comment: Accepted for Electric Power Systems Research. arXiv admin note: text overlap with arXiv:1901.0950