Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

An empirically-validated methodology to simulate electricity demand for electric vehicle charging

Plug-in Electric Vehicles (PEVs) are a clean form of transportation that will require a new control paradigm between the energy supply and the transportation sectors as PEV adoption continues to grow. Meeting the projected PEV energy demands will require implementing PEV / grid integration methods beyond charging PEVs at incentivized times. MATLAB was used to simulate the daily vehicle trip demands and charging processes of hundreds of PEVs. The National Household Travel Survey (NHTS) data was used as the source of vehicle travel data, and EV Project provided measured PEV charging data to calibrate the PEV population models. GridLAB-D analyses used a prototypical feeder and PEV population models to evaluate residential PEV V1G (unidirectional) charging in multiple locations, V1G PEV distribution feeder impacts, Time-Of-Use (TOU) rate effects on PEV charging, and charging control methods. The key findings were: optimized charging using California TOU rates could result in savings of over $20 per month for non-TOU program participants; non-TOU participants consume 30-40% of PEV charging energy; about 50% of PEV drivers have their transportation fuel bill reduced by 50 dollars per month; 6.6kW charging will cause distribution transformer power limits to be exceeded on prototypical feeders if the household PEV adoption rate reaches 0.75 PEVs per home; distribution feeder loading is proportional to PEV charging rate; a method was devised to remotely identify when residential transformer overloads occur using changes in PEV charging rate; and the low-cost TOU period is composed of a distribution of PEVs needing small, medium and larger energy needs that can be more effectively shifted as groups to mitigate the TOU peak power and enable a higher PEV adoption rate per household without overloading distribution transformers.

Unit commitment is a key issue in power system operation and has long been an intractable problem due to its complex mix-integer nonlinear formulation. The original unit commitment problem aims to minimize the fossil fuel cost by determining the on/off status of power units and power contribution of each online unit at the same time. However, the uncoordinated large charging power necessity of plug-in electric vehicles brings unprecedented challenges to the power system operators and further complicates the unit commitment problem. To seamless integrate the plug-in electric vehicles into the unit commitment, a new binary/real-value hybrid meta-heuristic algorithm framework is proposed in this paper, simultaneously determining the binary status and power output of units as well as the power delivered to/feedback from flexible charging and discharging of plug-in electric vehicles. A batch of binary particle swarm optimisation variants with different transfer functions are implemented and compared in solving the unit commitment problem with and without plug-in electric vehicles. Numerical studies illustrate the effectiveness of the proposed intelligent algorithm and the impact of different transfer functions is evaluated.

This paper presents a model predictive control (MPC) for off-board plug-in electric vehicle (PEV) chargers with photovoltaic (PV) integration using two-level four-leg inverter topology. The PEV charger is controlled by a unified controller that incorporates direct power and current MPC to dynamically control decoupled active-reactive power flow in a smart grid environment as well as to control PEV battery charging and discharging reliably. PV power generation with maximum power tracking is seamlessly integrated with the power flow control to provide additional power generation. Fast dynamic response and good steady-state performance under all power flow modes and various environmental conditions are evaluated and analyzed. From the results obtained, the charger demonstrates less than 1.5% total harmonic distortion as well as low active and reactive power ripple of less than 7% and 8% respectively on the grid for all power flow modes. The PEV battery also experiences a low charging and discharging current ripple of less than 2.5%. Therefore, the results indicate the successful implementation of the proposed charger and its control for PV integrated off-board PEV chargers.

Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization

Plug-in Electric Vehicles (PEV) integration into innovative smart grids has been studied widely. Recently, special attention is paid to the role of the aggregation agent, which might be responsible of controlling the charge schedules to its own and the system's benefit. Furthermore, an increasing awareness of the stochasticity involved in PEV charging, reflected in a number of recent publications, can be observed. Among others, the PEV energy retail problem with interactions in day-ahead and balancing markets has been formulated from the aggregator's perspective, taking into account location dependent network tariffs in the form of capacity prices for active power. However, a numerical quantification of the benefit from accounting for stochasticity has yet to be carried out. Therefore, this paper uses standard methodology to calculate stochastic programming quality metrics such as the value of the stochastic solution (VSS) and the expected value of private information (EVPI) for an established PEV energy retail aggregator model. Applied to a real medium voltage system with urban characteristics and realistic spatial PEV mobility, different levels of mobility forecasts are included as information at the first-stage here and now decisions.

Plug-in electric vehicles (PEVs) represent economic and eco-friendly means of transportation. High PEV penetration levels are expected during 2020 – 2050. Consequently, a considerable portion of the total power consumption in the future smart grid will be due to the PEV charging demands. Therefore, PEV charging has drawn significant attention from both academia and industry. In this paper, we shed some light on key research issues related to PEV charging in the future smart grid. Specifically, we discuss the challenges related to charging facility planning and PEV charging coordination for both mobile and parked vehicles. In addition, we summarize our main research results related to the aforementioned topics. Finally, future research directions are provided.

Smart grids have been introduced to replace conventional power distribution systems without real time monitoring for accommodating the future market penetration of plug-in electric vehicles (PEVs). When a large number of PEVs require simultaneous battery charging, charging coordination techniques have become one of the most critical factors to optimize the PEV charging performance and the conventional distribution system. In this case, considerable computational complexity of a central controller and exchange of real time information among PEVs may occur. To alleviate these problems, a novel threshold-based random charging (TBRC) operation for a decentralized charging system is proposed. Using PEV charging thresholds and random access rates, the PEVs themselves can participate in the charging requests. As PEVs with a high battery state do not transmit the charging requests to the central controller, the complexity of the central controller decreases due to the reduction of the charging requests. In addition, both the charging threshold and the random access rate are statistically calculated based on the average of supply power of the PEV charging system that do not require a real time update. By using the proposed TBRC with a tolerable PEV charging degradation, a 51% reduction of the PEV charging requests is achieved.

Smart grid architecture is one of the difficult constructions in electrical power systems. The main feature is divided into three layers; the first layer is the power system level and operation, the second layer is the sensor and the communication devices, which collect the data, and the third layer is the microprocessor or the machine, which controls the whole operation. This hierarchy is working from the third layer towards first layer and vice versa. This paper introduces an eco unit commitment study, that scheduling both conventional power plants (three IEEE) thermal plants) as a dispatchable distributed generators, with renewable energy resources (wind, solar) as a stochastic distributed generating units; and plug-in electric vehicles (PEVs), which can be contributed either loads or generators relied on the charging timetable in a trustworthy unit commitment. The target of unit commitment study is to minimize the combined eco costs by integrating more augmented clean and renewable energy resource with the help of field programming gate array (FPGA) layer installation. A meta-heuristic algorithm, such as the Gravitational Search Algorithm (GSA), proves its accuracy and efficiency in reducing the incorporated cost function implicating costs of CO2 emission by optimally integrating and scheduling stochastic resources and charging and discharging processes of PEVs with conventional resources power plants. The results obtained from GSA are compared with a conventional numerical technique, such as the Dynamic Programming (DP) algorithm. The feasibility to implement GSA on an appropriate hardware platform, such as FPGA, is also discussed.

Coordinated charging of Plug-In Electric Vehicles (PEVs) in residential distribution systems is a new concept currently being explored in the wake of smart grids. Utilities are exploring these options as there are concerns about potential stresses and network congestions that may occur with random and uncoordinated multiple domestic PEV charging activities. Such operations may lead to degraded power quality, poor voltage profiles, overloads in transformer and cables, increased power losses and overall a reduction in the reliability and economy of smart grids. Future smart grids communication network will play an important role in PEV operation because the battery chargers can be remotely coordinated by the utility and harnessed for storing surplus grid energy and reused to support the grid during peak times. Based on a recently proposed PEV charging algorithm, this paper focuses on the impact of coordinated charging on distribution transformer loading and performance. Simulation results are presented to explore the ability of the PEV coordination algorithm in reducing the stress on distribution transformers at different PEV penetration levels. The performance of various distribution transformers within the simulated smart grid is examined for a modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.

The massive and random charging activities by increasing penetration of the plug-in electric vehicles (PEVs) shall bring significant peak load in the near future. And the massive Greenhouse Gas (GHG) emission caused by electricity generation also brings challenges to the environment. And the charging management mechanism therefore is the key to charging PEVs with low GHG emission and help utility operators optimize generation strategy. The paper firstly develops an hourly dispatch model to simulate GHG emissions caused by electricity generations, and an access network structure composing submeters and smart meter mesh network is proposed. Then a novel coordinated charging management mechanism using queuing theory is implemented to minimize the PEV charging load impact to the electricity grid. To assess the mechanism performance, the paper constructs an hourly PEV charging model to simulate the grid impact and GHG emissions. And the two detailed and comprehensive scenarios with incoordinated and coordinated charging mechanism are simulated while key parameters are generated, analyzed, and approved. Simulation results show that the coordinated charging mechanism works efficiently on reducing the grid peak load, improving the utility utilization, and reducing GHG emission. Study findings in this paper will help support informed decision-making regarding PEV development and deployment in support of national energy and environmental policy.

Plug-in electric vehicles (PEVs) could provide valuable ancillary service for power systems with intermittent renewables. This paper proposes a charging model of a large amount of PEVs to mitigate the high-level wind power fluctuations in a distribution network so as to regulate system frequency. With the prerequisite that all PEV daily driving patterns are completely satisfied, the PEV charging power is economically allocated to counterbalance the wind generation intermittency. Afterward, a center-free control scheme based on the consensus algorithm is designed for PEVs to share the fluctuating wind generation in a fully distributed manner. The scheme is robust and flexible to practical PEV charging behaviors including arrival and departure time, initial and desired SOC, as well as the frequently changed departure time. Comprehensive simulations on a distribution system with coal and diesel generators, several wind farms and 2000 PEVs demonstrate that the proposed PEV charging scheme could effectively regulate the system frequency in the real time while the PEV users’ charging requirements could be flexibly satisfied.

This article presents the performance analysis of future Provincial Electricity Authority of Thailand (PEA) distribution networks under high penetration of plug‐in electric vehicle (PEV) home charging using the Monte Carlo method. Network performance indices considered in this study are the voltage profile, power losses, load factor, and voltage unbalance. The voltage profile and power losses are evaluated based on the PEA criteria. In addition, the voltage unbalance factors (%VUF) are evaluated according to the IEC 61000‐2‐4: 2000‐06 standards. A selected PEA distribution network based on the existing data is used for the simulation study with the DIgSILENT PowerFactory. The penetration of PEV home charging at each node and phase is assigned using the Monte Carlo method, which in particular is used to randomly generate patterns of PEV charging behaviors. The simulation study presents five cases; the average points of charge, max‐to‐min charge, min‐to‐max charge, central feeder charge, and random point of charge. All cases are compared with a base case (without PEVs). Charging scenarios at phases ‘a’, ‘b’, and ‘c’ are assumed having overlapped time duration. The PEV charging duration per phase is assumed 6 h. Simulation results indicate that PEV charging time, charging point, and penetration levels at each phase and node are significant factors impacting the network performance. Voltage profiles slightly drop in all cases, meanwhile the average voltage drop increases by 7% compared with the base case; case 3 has the maximum voltage drop, which is deeper than PEA criteria. The power losses increase in all cases. The average power losses have increased by 103.52%. The %VUF increases gradually with the length of the feeder. The maximum %VUF occurs at the end of the feeder for all cases and is higher than 2.0% of the IEC standard. On the other hand, PEV charging improves the load factor. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Bidirectional coordinating dispatch of large-scale V2G in a future smart grid using complementarity optimization

The geographically spatial and controlled distribution of fossil fuel resources, catastrophic global warming, and depletion of fossil fuel resources have forced us to integrate zero- or low-emissions energy resources, such as wind and solar, in the generation mix. These renewable energy resources are unexhausted, available around the globe, and free of cost. The advancement in wind and solar technologies has caused an appreciable decrease in installed the and global levelized costs of electricity via these sources. Therefore, the penetration of renewable energy resources in the generation mix can provide a promising solution to the above-mentioned problems. The aim of simultaneously reducing fuel consumption in terms of “Fuel Cost” and “Emission” in thermal power plants is called a combined economic emission dispatch problem. It is a combinatorial and multi-objective optimization problem. The solution of this problem is to allocate the load demand and losses on the committed units in such way that the overall costs of the generation and emission of thermal units are reduced, while the legal bounds (constraints) are met. It is a highly non-linear and complex optimization problem. The valve-point loading effect makes this problem non-convex. The addition of renewable energy resources (RERs) adds more complexities to this problem because they are intermittent. In this work, chaotic salp swarm algorithms (CISSA) are used to solve the combined economic emission dispatch problem. Chaos is used as an alternative to randomization for the tuning of the control variable to improve the trait of obtaining global extrema. Different test cases having different combinations of thermal, solar, and wind units are solved using the proposed algorithm. The results show the superiority of this study in comparison to the existent research results in terms of the cost of generation and emissions.